用于储层参数预测的神经网络模式识别方法研究
|
摘要
石油的产生有着不可重复性,不可实验性的特点。人们对地下油气储层的认识存在相当程度的模糊性和不确定性。由于某些不正确的认识会带来巨额生产资金消耗。研究准确识别与预测储层及参数空间变化的方法,在油气勘探开发中都具有重要的实际意义,可以提高生产效益,为国家节约巨额的资金。
本文以油田的科技攻关项目为背景,以准确、清晰地再现地下油气储层及参数空间展布,提高油气勘探开发的准确率,节约生产成本,提高生产效率为目标,将计算机模式识别技术和现代预测理论与石油生产相结合,围绕对地下油气储层及参数的识别与预测进行一系列的研究工作。主要内容如下:
1.在系统分析研究人工神经网络原理的基础上,对误差回传神经网络(BP)的结构、计算公式进行了深入研究。针对地下储层参数与地震特征参数是复杂的非线性关系,很难用精确的表达式表示出来这一问题,指出了神经网络以其自身的非线性逼近能力是解决上述问题的有利工具,同时给出了神经网络进行储层参数预测的过程描述。
2.在深入分析径向基函数(RBF)神经网络和遗传算法理论的基础上,提出了一个基于自适应遗传算法的径向基函数神经网络的结构优化算法,并将该算法成功地应用于储层参数的横向预测,取得了非常好的效果。该算法将基函数的中心参数和宽度参数编成染色体,将网络结构优化和参数学习分两个阶段进行,训练和进化。先用梯度下降法学习某一染色体对应的网络的中心和宽度参数,而后用最小二乘法学习网络的线性权值和偏移常数;再用遗传进化算法优化隐节点数。通过这两个过程的交替进行,得到满足误差要求的具有最小隐节点数的并且隐节点基函数具有不同宽度参数的RBF神经网络。
3.深入研究分析小波变换理论和边缘检测原理,并将地震数据剖面看成是二维数字图像,地震剖面上的反射界面相当于二维数字图像的边缘。首次提出将小波变换模极大边缘检测技术应用到地震剖面图像的特殊处理上,使用于储层研究的地震资料具有较高的质量,即高信噪比、高分辨率。从而使计算机能更准确、可靠地预测储层参数的空间展布,提高勘探开发的精确度。用该方法对实际资料进行了处理,取得了较好的效果。
4.在深入研究分析小波神经网络的结构、性质、算法和逼近能力的基础上,同时通过小波神经网络与常用网络的比较,提出了用于储层参数预测的小波神经网络优化算法,得到了较为优化的网络结构,并给出了系统的学习算法。由于人工神经网络具有自学习、自适应、鲁棒性、容错性和扩充性能,而小波分析具有时频局部特性和变焦特性,将两者的优势相结合,使得小波神经网络具有较强的逼近、容错能力。实际资料预测表明,本文提出的用于储层参数预测的小波神经网络具有收敛速度快,逼近非线性能力强的优点。
5.在深入分析模糊系统理论和神经网络的基础上,针对神经网络和模糊系统各自的优点和不足,对模糊系统和神经网络的融合技术进行了深入研究,结合火山岩储层识别预测的特点和难点,提出了用于火山岩储层识别预测的模糊神经网络系统,同时给出了系统的结构和参数学习算法及识别预测火山岩储层的步骤。在方法研究过程中,充分利用现有的样本数据,发挥神经网络的自适应学习能力和模糊系
Petroleum cannot be reformed by experiment, and man cannot made it repeatedly underground. Man havesome unclear and indeterminacy cognition about underground oil reservoir, it make waste a lot of money everyyear. So it is very important that man realize the structure and parameter distribution of oil reservoir clearly. Itcan heighten production efficiency and save a lot of money .
In this paper , some scientific research item of oil field are used as the background. And it is used asobject that the modality of oil reservoir is reappeared clearly and the accuracy rating of exploration isimproved. We study the pattern recognition theory and the methods for accurate fast prediction the parametersof oil reservoir. The main contents are as follows:
1. After analyse and study systematically artificial neural network theory, the structure and computeformula of BP is studied. Considering the nonlinear relation between reservoir parameter and seismiccharacteristic parameters, the neural network is a advantageous tool to solve above problem, the coursedescription of reservoir parameter predicting using neural network is provided.
2. Based on studying radical basic function neural network and genetic algorithm, a optimize method ofradical basic function neural network based on self-adapting genetic algorithm is proposed, and this method isapplied successful to the reservoir parameter predicting and obtain better effect. In this method, the centerparameter and width parameter are considered chromosome, the network structure optimize and parameterlearning are divided into training and evolution two parts. At first, learning the center parameter and widthparameter using gradient descend algorithm, then learning the linear weight value and offset constant usingleast square algorithm. Secondly, optimize the hidden node numbers using self-adapting genetic algorithm.Finally, we can obtain the RBF neural network which satisfy the error demand by alternate executing abovetwo courses.
3. The wavelet transform theory and edge detection principle is in-deep studied, the seismic sections usedfor seismic interpretation are actually regarded as some 2D images, so the reflection interface of seismicsection can be considered as edges on the seismic section images. The wavelet transform Modulus-Max edgedetection algorithm which applied to seismic section processing is proposed first, its purpose is to eliminatethe random interference and enhanced the quality of seismic section and the signal-to-noise rate and resolutionof seismic signal, furthermore, predicting the parameter distribution of oil reservoir accurately and credibility.This algorithm is used to process actual data and obtain better effect.
4. Based on analyse and studying the structure and property and approach ability of wavelet neuralnetwork , a method of wavelet neural network which is used to predict reservoir parameter is proposed ,meanwhile, the optimize network structure and learning algorithm of network are presented. Artificial neuralnetwork has the capability of self-learning and self-adapting and approaching nonlinear, the wavelet transformhas good partial property both in time domain and frequency domain, so the wavelet neural network whichcombining the advantage of ANN and wavelet has stronger approaching nonlinear capability and allowanceerror capability. The actual data predicting shows that this method provides higher predict accuracy and fasterconvergence speed.5. The fuzzy system theory is studied, considering to the advantage and disadvantage of the neuralnetwork and fuzzy system, the fusion problem of fuzzy system and neural network is in-deep studied,combining the salient feature and difficulty of volcanic reservoir, a fuzzy neural network system which appliedrecognition and predicting the volcanic reservoir is presented, meanwhile, the system structure and parameterlearning algorithm and predicting steps are given. In this thesis, stronger self-learning ability of neural networkand good express ability of fuzzy system are combined to recognise and predict the volcanic reservoir. Actualapplication result appear that this method recognise and predict the volcanic reservoir accurately and fast.6. Based on studying the statistical learning theory and machine learning and support vector machineprincipal, considering the ANN has some problems in application such as converge to local minimum, theoverfitting and the structure of ANN is always decided by experience because it doesn't have a good guidingtheory, especially, when the number of the training sample is not enough, the predicting accuracy will beinfluenced, a method of recognition and predicting the volcanic reservoir based on regression support vectormachine(SVM) is proposed first. SVM is a newly developed technique which based on statistical learningtheory, it adopts Structure Risk Minimization principle which avoids the disadvantages of ANN. In this thesis,the recognition and predicting of regression algorithm based on SVM is studied, the fast learning algorithm isstudied and the concrete realization is presented. Actual application result shows that this method remedy thescarcity of few sample data, overcome the disadvantages of traditional neural network, therefore, theconverging speed and the predicting accuracy of system are enhanced.7. Programming some programmes using C++ such as the radical basic function neural network based onself-adapting genetic algorithm, the wavelet transform Modulus-Max edge detection algorithm which appliedto seismic section processing, the wavelet neural network algorithm which is used to predict reservoirparameter, the fuzzy neural network system which applied recognition and predicting the volcanic reservoir,the recognition and predicting the volcanic reservoir based on regression support vector machine algorithm.
本文以油田的科技攻关项目为背景,以准确、清晰地再现地下油气储层及参数空间展布,提高油气勘探开发的准确率,节约生产成本,提高生产效率为目标,将计算机模式识别技术和现代预测理论与石油生产相结合,围绕对地下油气储层及参数的识别与预测进行一系列的研究工作。主要内容如下:
1.在系统分析研究人工神经网络原理的基础上,对误差回传神经网络(BP)的结构、计算公式进行了深入研究。针对地下储层参数与地震特征参数是复杂的非线性关系,很难用精确的表达式表示出来这一问题,指出了神经网络以其自身的非线性逼近能力是解决上述问题的有利工具,同时给出了神经网络进行储层参数预测的过程描述。
2.在深入分析径向基函数(RBF)神经网络和遗传算法理论的基础上,提出了一个基于自适应遗传算法的径向基函数神经网络的结构优化算法,并将该算法成功地应用于储层参数的横向预测,取得了非常好的效果。该算法将基函数的中心参数和宽度参数编成染色体,将网络结构优化和参数学习分两个阶段进行,训练和进化。先用梯度下降法学习某一染色体对应的网络的中心和宽度参数,而后用最小二乘法学习网络的线性权值和偏移常数;再用遗传进化算法优化隐节点数。通过这两个过程的交替进行,得到满足误差要求的具有最小隐节点数的并且隐节点基函数具有不同宽度参数的RBF神经网络。
3.深入研究分析小波变换理论和边缘检测原理,并将地震数据剖面看成是二维数字图像,地震剖面上的反射界面相当于二维数字图像的边缘。首次提出将小波变换模极大边缘检测技术应用到地震剖面图像的特殊处理上,使用于储层研究的地震资料具有较高的质量,即高信噪比、高分辨率。从而使计算机能更准确、可靠地预测储层参数的空间展布,提高勘探开发的精确度。用该方法对实际资料进行了处理,取得了较好的效果。
4.在深入研究分析小波神经网络的结构、性质、算法和逼近能力的基础上,同时通过小波神经网络与常用网络的比较,提出了用于储层参数预测的小波神经网络优化算法,得到了较为优化的网络结构,并给出了系统的学习算法。由于人工神经网络具有自学习、自适应、鲁棒性、容错性和扩充性能,而小波分析具有时频局部特性和变焦特性,将两者的优势相结合,使得小波神经网络具有较强的逼近、容错能力。实际资料预测表明,本文提出的用于储层参数预测的小波神经网络具有收敛速度快,逼近非线性能力强的优点。
5.在深入分析模糊系统理论和神经网络的基础上,针对神经网络和模糊系统各自的优点和不足,对模糊系统和神经网络的融合技术进行了深入研究,结合火山岩储层识别预测的特点和难点,提出了用于火山岩储层识别预测的模糊神经网络系统,同时给出了系统的结构和参数学习算法及识别预测火山岩储层的步骤。在方法研究过程中,充分利用现有的样本数据,发挥神经网络的自适应学习能力和模糊系
Petroleum cannot be reformed by experiment, and man cannot made it repeatedly underground. Man havesome unclear and indeterminacy cognition about underground oil reservoir, it make waste a lot of money everyyear. So it is very important that man realize the structure and parameter distribution of oil reservoir clearly. Itcan heighten production efficiency and save a lot of money .
In this paper , some scientific research item of oil field are used as the background. And it is used asobject that the modality of oil reservoir is reappeared clearly and the accuracy rating of exploration isimproved. We study the pattern recognition theory and the methods for accurate fast prediction the parametersof oil reservoir. The main contents are as follows:
1. After analyse and study systematically artificial neural network theory, the structure and computeformula of BP is studied. Considering the nonlinear relation between reservoir parameter and seismiccharacteristic parameters, the neural network is a advantageous tool to solve above problem, the coursedescription of reservoir parameter predicting using neural network is provided.
2. Based on studying radical basic function neural network and genetic algorithm, a optimize method ofradical basic function neural network based on self-adapting genetic algorithm is proposed, and this method isapplied successful to the reservoir parameter predicting and obtain better effect. In this method, the centerparameter and width parameter are considered chromosome, the network structure optimize and parameterlearning are divided into training and evolution two parts. At first, learning the center parameter and widthparameter using gradient descend algorithm, then learning the linear weight value and offset constant usingleast square algorithm. Secondly, optimize the hidden node numbers using self-adapting genetic algorithm.Finally, we can obtain the RBF neural network which satisfy the error demand by alternate executing abovetwo courses.
3. The wavelet transform theory and edge detection principle is in-deep studied, the seismic sections usedfor seismic interpretation are actually regarded as some 2D images, so the reflection interface of seismicsection can be considered as edges on the seismic section images. The wavelet transform Modulus-Max edgedetection algorithm which applied to seismic section processing is proposed first, its purpose is to eliminatethe random interference and enhanced the quality of seismic section and the signal-to-noise rate and resolutionof seismic signal, furthermore, predicting the parameter distribution of oil reservoir accurately and credibility.This algorithm is used to process actual data and obtain better effect.
4. Based on analyse and studying the structure and property and approach ability of wavelet neuralnetwork , a method of wavelet neural network which is used to predict reservoir parameter is proposed ,meanwhile, the optimize network structure and learning algorithm of network are presented. Artificial neuralnetwork has the capability of self-learning and self-adapting and approaching nonlinear, the wavelet transformhas good partial property both in time domain and frequency domain, so the wavelet neural network whichcombining the advantage of ANN and wavelet has stronger approaching nonlinear capability and allowanceerror capability. The actual data predicting shows that this method provides higher predict accuracy and fasterconvergence speed.5. The fuzzy system theory is studied, considering to the advantage and disadvantage of the neuralnetwork and fuzzy system, the fusion problem of fuzzy system and neural network is in-deep studied,combining the salient feature and difficulty of volcanic reservoir, a fuzzy neural network system which appliedrecognition and predicting the volcanic reservoir is presented, meanwhile, the system structure and parameterlearning algorithm and predicting steps are given. In this thesis, stronger self-learning ability of neural networkand good express ability of fuzzy system are combined to recognise and predict the volcanic reservoir. Actualapplication result appear that this method recognise and predict the volcanic reservoir accurately and fast.6. Based on studying the statistical learning theory and machine learning and support vector machineprincipal, considering the ANN has some problems in application such as converge to local minimum, theoverfitting and the structure of ANN is always decided by experience because it doesn't have a good guidingtheory, especially, when the number of the training sample is not enough, the predicting accuracy will beinfluenced, a method of recognition and predicting the volcanic reservoir based on regression support vectormachine(SVM) is proposed first. SVM is a newly developed technique which based on statistical learningtheory, it adopts Structure Risk Minimization principle which avoids the disadvantages of ANN. In this thesis,the recognition and predicting of regression algorithm based on SVM is studied, the fast learning algorithm isstudied and the concrete realization is presented. Actual application result shows that this method remedy thescarcity of few sample data, overcome the disadvantages of traditional neural network, therefore, theconverging speed and the predicting accuracy of system are enhanced.7. Programming some programmes using C++ such as the radical basic function neural network based onself-adapting genetic algorithm, the wavelet transform Modulus-Max edge detection algorithm which appliedto seismic section processing, the wavelet neural network algorithm which is used to predict reservoirparameter, the fuzzy neural network system which applied recognition and predicting the volcanic reservoir,the recognition and predicting the volcanic reservoir based on regression support vector machine algorithm.
引文
[1] Meculloch, W.S. and W.H.Pitts. A logical calculus of ideas immanent in nervous activity. Bulletin of Math .Biophsics. 1943, 5: 115-133
[2] Kohonen, T. K. Self-Organizayion and Associative Memory. New York.Spring-Verlag. 1988
[3] Rumelhart, D. E, and McCIelland (eds) . Parallel Distributed Processing, volume 1 and Volume 2. MIT Press,Cambridge. 1986
[4] Rosenblatt, F. Principles of Neurodynamics. Spartan New York. 1962
[5] Hinton, J. E. T. J. Sejnowski. Learning and Relearning in Boltzmann Machine MIT Press. Cambridge MA. 1986
[6] Hebb, D.D, The Organization of Behavior. John Wiley & Sons. New York. 1949
[7] Widrow, B. M. E. Hoff. WESCON Convention Record. 1960, 4: 99-104
[8] Minsky, M. and S. Papert, Perception: An Introduction to Computation Geometry.Mit Press, Cambridge. Massachusetts. 1969
[9] T.Kohonen, Associative Memory :A System Theory Approach. Springer. New York. 1977
[10] T.Kohonen, Cognitron : A Self-Organization Multilayered Neural network. Biol. Cyber. 1975, (20): 121-136
[11] Widrow. B. S.D.stearns, Adaptive Signal Processing. Prencetice-Hall. Englewood Cliffs, NJ. 1985
[12] Marlsburg. Cvonder. Self-Organization of Orientation Sensitive Cell in the Striated Cortex. Kybernetic. 1973, (14): 85-100
[13] Grossberg.S. Adaptive .Pattern Classification and Universal Recording. Boil. Cybernet. 1976, 23: 121-134
[14] Fukushima, K. A Self-Organizing Multilayered Neural Networks.Boil. Cybernet. 1975, 20: 121-136
[15] Hopfild, J. J. Neural network and physical systems with emergent collective computational abilities. Proc .Natl.Acad.Sci.USA. 1982, 79: 1552-2558
[16] DAPRA Neural Network Study . AFCEA international Press. 1988
[17] K.Fukushima, Neocognition: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cyber. 1980, 36(4): 193-202
[18] K.Fukushima and S.Miyake, Necognitron :A New Algorithm for Pattern Recognition Tolerant of Deformation and Shifts in Position.Pattern Recognition. 1982, 15(6): 455-469
[19] F.L. Lewis, K. Liu, and A. Yesildirek. Neural net robot controller with guaranteed tracking Performance. IEEE Trans. Neural Networks. 1995, 703-710
[20] Ahmed Tarraf and Martin Meyers;Neural Network-Based Voice Quality Measurement Technique. Proceedings of the The Fourth IEEE Symposium on Computers and Communications. 1998
[21] S. McLoone, M.D. Brown, G. Irwin and G. Lightbody. A Hybrid Linear/Nonlinear training Algorithm for Feedforward Neural Networks. IEEE Transactions on Neural Networks. 1998, 9(9): 669-683
[22] K.S. Narendra, Adaptive Control Using Neural Networks. Neural Networks for Control. MIT Press. 1991, 115-142
[23] L.G. Kraft and D.P. Campagna, A Summary Comparison of CMAC Neural Networks and Traditional Adaptive Control Systems. Neural Networks and Control. MIT Press. 1991, 143-169
[24] S. Jagannathan and F.L. Lewis. Multilayer discrete-time neural net controller with guaranteed performance. IEEE Trans. Neural Networks. to appear. 1995
[25] M.S. Dawson, A.K. Fung, and M.T. Manry. Surface Parameter Retrieval Using Fast Learning Neural Networks. Remote Sensing Reviews. 1993, 7: 1-18
[26] X. Jiang, Mu-Song Chen, M. T. Manry, M.S. Dawson, A.K. Fung. Analysis an Optimization of Neural Networks for Remote Sensing. Remote Sensing Reviews. 1994, 9: 97-114
[27] M.T. Manry, S.J. Apollo, L.S. Allen, W.D. Lyle, W. Gong, M.S. Dawson, and A.K. Fung. Fast Training of Neural Networks for Remote Sensing. Remote Sensing Reviews. 1994, 9: 77-96
[28] A. Khotanzad, R-C Hwang, and D. Maratukulam. Hourly Load Forecasting by Neural Networks. IEEE PES Winter Meeting. Columbus Ohio. February. 1993
[29] 中国地质学会数学地质专业委员会编. 中国数学地质. 1998,8
[30] 陆文凯,李衍达,谢军等. 地层真电阻率恢复神经网络法. 测井技术. 1999,23(1): 19-23
[31] 程方道. 神经网络在地球物理反演中的应用. 地球物理学进展. 1994, 9(3): 51-59
[32] 陆文凯,李衍达等. 误差反向传播神经网络法地震反演. 地球物理学报. 1996, 39: 293-310
[33] 焦李成编著. 神经网络的应用与实现. 西安:西安电子工业出版社. 1996
[34] 焦李成著. 神经网络系统理论. 西安:西安电子科技大学出版社. 1990
[35] 赵振宇,徐用懋著. 模糊理论和神经网络的基础和应用. 北京:清华大学出版社. 1996
[36] 刘震编著.储层地震地层学. 北京:地质出版社. 1997.
[37] 袁秉衡主编. 应用地震技术研究储层. 北京: 石油工业出版社. 1992.
[38] 于兴河,李剑峰. 碎屑岩系储层地质建模及计算机模拟.北京:地质出版社,1996.
[39] 陈恭洋. 碎屑岩储层随机建模. 北京:地质出版社,2000.
[40] 邓宏文,王红亮,祝永军著. 高分辨率层序地层学原理及应用.北京:地质出版社,2002
[41] 曹建福,韩崇昭,方洋旺. 非线性系统及应用[M]. 西安:西安交通大学出版社,2001
[42] 李正文,李琼,吴朝容. 沉积盆地有效储集层综合识别技术[M]. 成都:四川科技出版社,2002, 44-57
[43] 邱家骧,陶奎元,赵俊磊,等编著.火山岩[M].北京:地质出版社, 1996. 1-58
[44] 陶奎元. 火山岩相构造学[M] . 南京:江苏科技出版社,1994. 20-551
[45] 李双成,郑度. 人工神经网络模型在地学研究中的应用进展. 地球科学进展.2003,18(1)
[46] 刘宪斌, 林金逞. 地震储层研究的现状及展望. 地球学报. 2002, 23(1):73-78
[47] 侯贵卿, 孙萍. 油气储层研究新动向. 海洋石油. 2000. Vol.103(1):8-14
[48] 吴朝容,李正文. 地震多参数油气预测方法及应用研究. 沉积与特提斯地质.2002,22(4).
[49] 冉建斌,李建雄. 基于三维地震资料的油藏描述技术和方法. 石油地球物理勘探.2004,39(1).
[50] 许 多 , 李 显 贵 , 刘 殊 等 . 合 兴 场 地 震 资 料 吸 收 油 气 预 测 研 究 [J]. 物 探 化 探 计 算 技术,2000 ,22(1) :56-58
[51] 王斌,李显贵,文雪康. 动态时窗多参数油气预测技术在柏桠的应用[J]. 物探化探计算技术,2000 ,22(1) : 179-183
[52] 冉建斌. 精细油藏描述技术在油藏滚动勘探开发中的应用. 勘探家,2001 ,4.
[53] 吕晓光,杜庆龙. 应用人工神经网络模型进行孔隙度和渗透率参数预测[J]. 大庆石油地质与开发,1996 ,15 (3) :27-31
[54] 蒋建平, 康贤. 层物性参数展布的相控模型[J].成都理工学院学, 1995,22 (1):12-17
[55] 邱家骧. 国际地科联火成岩分类学分委会推荐的火山岩分类简介[J]. 现代地质, 1991 , 5 (4) :457-468
[56] 王永刚等.利用地震信息预测储层裂缝发育带.石油物探, 2000, 39
[57] 赵海玲. 火山岩储层. 现代地质,1998,12(1):62
[58] 李长山, 陈建文. 火山岩储层建模初探. 地学前缘. 2000, Vol.7(4):381-388
[59] 李 明 , 邹 才 能 . 松 辽 盆 地 北 部 深 层 火 山 岩 气 藏 识 别 与 预 测 技 术 . 石 油 地 球 物 理 勘探.2002,37(5):477-484
[60] 刘启 , 舒萍 . 松辽盆地北部深层火山岩气藏综合描述技术 . 大庆石油地质与开发 . 2005,24(3):21-23
[61] 何 琰, 伍友佳. 火山岩油气藏研究[J]. 大庆石油地质与开发, 1999, 18 (4) : 6-8
[62] 张晓东, 霍 岩. 松辽盆地北部地区火山岩特征及分布规律[J]. 大庆石油地质与开发, 2000, 19 (4): 10-12
[63] Gardner M W. Dorling S R. Artificial neural networks (multilayer perceptron) —A review of applications in the atmospheric sciences[J ]. Atmospheric Environment,1998,32 (14～15):2627-2636
[64] Bahorich M and Farmer S. 3-D seismic discontinuity for faults and stratigraphic features : The coherence cube [ J ] . The Leading Edge , 1995. 14 (10) :1053-1058
[65] Chen Q , Sideney S. Seismic attribute technology for reservoir forecasting and monitoring [J] . The Leading Edge , 1997.16 ( 5) : 445-456
[66] Jan Helgesen. Comparison of constrained sparse spike and stochastic inversion for porosity prediction at Kristin Field. The Leading Edge ,2000 (4).
[67] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties ,part 1 :A da2ta interpretation methodology. T EL ,1994 ,13 (5) :305-310
[68] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties ,Part 2 :Using artificial neural networks for nonlinear attribute calibra tion1 T EL ,1994 ,13 (5) :674-678
[69] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties , Part 3 : A controlled study1 T EL ,1994 , 13 (5) :770-776
[70] Kurt J M 1 3D seismic at tributes using a semblancebased coherency algorithm. Geophysics, 1998, 63.
[71] Levin L E. Volcanogenic and volcaniclastic reservoir rocks inMesozoic -Cenozoic island arcs: examp les from the Caucasus and the NW Pacific [ J ] 1Journal of Petroleum Geology, 1995, 18 (3 ) : 267-288
[72] 边肇祺, 张学工编著. 模式识别(第二版)[M]. 北京: 清华大学出版社. 2000.
[73] 傅京孙主编. 模式识别应用. 北京大学出版社. 1990.
[74] 殷勤业等编译. 模式识别与神经网络. 机械工业出版社. 1992
[75] 熊承义, 李玉海. 统计模式识别极其发展现状综述. 科技进步与对策. 2003. 8. 173-175
[76] Zadeh, L. A. Fuzzy sets and their application to classification and clustering[M]. New York: Academic Press, 1977. 251-299
[77] Timothy J. Ross. Fuzzy logic with engineering applications[M]. New York: McGraw-Hill, 1995
[78] Paul S. Wu, Ming Li. Supervised and unsupervised fuzzy-adaptive Hamming net[J]. Patern Recognition,2002,32:1801-1816
[79] F.Behloul, B.PF.Lelieveldt, A.Boudraa, et al. Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data[J]. Patern Recognition,2002,35:659-675
[80] 卢涛, 陈德钊. 径向基网络的研究进展和评述. 计算机工程与应用. 2005.4:60-62
[81] 焦李成, 陈德钊. 基函数网络逼近:进展与展望. 工程数学学报. 2002.19(1):21-36
[82] 周峻武, 孙传荛等. 径向基函数网络(RBF)的研究及实现[J]. 矿冶. 2001.10(4):71-75
[83] 柴 杰 , 江 青 茵 等 . RBF 神 经 网 络 的 函 数 能 力 及 其 算 法 [J]. 模 式 识 别 与 人 工 智 能 . 2002.15(3):310-316
[84] Moody J, Darken C. Learning with the localized receptive fields. In: Proc, Connectionist Models Summer School, Camegie Mellon Univ. 1988
[85] Moody J, Darken C. Fast Learning in Networks of Locally-luned Processing Units[J]. Neural Computation,1989:I(1):281-294
[86] Powell M J D. Radial basic function approximations to polynomials[C]. In:12th Biennial Numerical Analysis Conf(Dundee),1987:223-241
[87] H. N. Mhaskar, C. A. Micchelli Approximation by Superposition of Sigmoidal and Radial Functions. Advances on Applied Mathematics, 1992, (13):350-373
[88] T. Chen, H. Chen and R. W. Liu Approximation Capacity in C(R") by Multi-layer. Feeforward Networks and Related Problems. IEEE Trans. On Neural Networks, 1995, 6(1): 185-198
[89] Moshe Leshno Multilayer Feedforward Networks with a Non-polynomial Activation Function Can Approximate Any Function. Neural Networks, 1993, 6: 861-867
[90] Chen TianPing, Chen Hong Approximation Theory Capability to Functions of Several Variables, Nonlinear Functions, and Operators by Radial Basis Functional Neural Networks. IEEE Trans. on Neural Networks, 1995, 6(4):904-910
[91] Broomhead, D. S., Lowe, D. Multivariable functional interpolation and adaptive networks. Complex systems, vol.2, pp: 321-335
[92] 张玉瑞,陈剑波. 基于 RBF 神经网络的时间序列预测.计算机工程与应用. 2005.11.74-76
[93] 姬晓飞,申东日,陈义俊. 基于改进的 RBF 神经网络在线辨识算法及其应用. 计算机仿真. 2003.Vol.20(11):61-63
[94] Sanner, R. M., Slotine, J.-J. E., (1994). Gaussian networks for direct adaptive control. IEEE Trans. On Neural networks, vol. 2, no. 6, pp: 837-863
[95] Darken C, Moody J. Fast adaptive K-mean clustering:some empirical results[C]. In: Proceeding International Joint Conference on Neural Networks, Piscataway: IEEE, Press,1990:238
[96] Lei Xu. Rival Pennalized Competitive Learning for Clustering Analysis, RBF Networks and Curve Dectection[J]. IEEE Trans on Neural Networks, 1993,4(4):636-649
[97] Chen S, Grant P M, Cowan C F N. Orthogonal Least Squares Learning Algorithm for training Multioutput Radial Basis Function Networks. IEEE Proceedings F,1992,139(6):378-384
[98] Chen S, Billings S A, Grant P M. Rescursive Hybrid Algorithm for Nonlinear Identification Using Radial Basis Function Networks[J]. International Journal of Control,1995,55(5):1051-1070
[99] B. Walezak, D L Massart. The Radial Basis Function-Partial Least Squares Approach as a Flexible Non-linear Regression Technique[J]. Analytical Chimical Acta,1996, 331:177-185
[100] Lang P M, Brencheley J M, Nieves R G et al. Cyclic Subspace Regression[J]. Journal of Multivariate Analysis, 1998,65:58-70
[101] Goldberg D E. Genetic algorithms in Search, optimization and machine reading[M]. MA:Addison-Wesley,1989
[102] Orvosh D, Davis L. Using Algorithm to Optimize Problems with Feasibility Constraints[C]. In:Proc of 1st IEEE Cont on Evolutionary Computation, 1994:548-553
[103] Chen H, Flann N S. Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybird Methods. In: Parallel Problem Solving from Nature 3, Springer –Verlag,1994:428-438
[104] Robert J , Schilling James , Carroll J . Approximation of nonlinear systems with radial basis function neural networks[J] . IEEE TRANSACTIONS ON NEURAL NETWORKS , 2001 , 12 (1) : 21-28
[105] Mukherjee S and Nayar S K.Automatic Generation of RBF Network Suing Wavelet. Pattern Recognition 2nd. 1996, 29(8): 1369-1383
[106] Chui C K.An introduction to Wavelets.New York:Academic Press. 1992
[107] Chui C K. Wavelet: a tutorial theory and applications. New York: Academic Press. 1992,1-432
[108] Daubechies I .Ten Lectures on Wavelets. Philadelphia. PA:SIAM Press. 1992
[109] Baskstlg R and Stephanopoubus C. Wavelet:a multiresolution,hierarchical neural network with localized learning.Amer Inist Chem Eng J. 1993, 39(1): 57-81
[110] Young R.An introduction to nonlinear Fourier series. New York,Academic Press. 1980: 1-150
[111] Roberto Kawakami Harrop Galvao and Takashi Yoneyama. A Neural Classifier Employing Biased Wavelets. Proceedings of the Vth Brazilian Symposium on Neural Networks. 1998
[112] Morlet.J. Wave Propation and Sampling theory and complex waves.Geophysics.1982, 47(2): 222-236
[113] Daubcchles .I. Orthonormal bases of compactly supported wavelets.Comm.on Pure and Appl.Math . 1988, 41(7): 909-996
[114] Burt P J,Adelson E.The Laplacian Pyramid as a compact image code.IEEE Trans on Communicatlons. 1983, 31(4): 532-540
[115] Mallat. S.A theory for multiresolutions signal decomposition :the wavelet representation.IEEE Trans.on PAMI. 1989, 1l(7): 674-693
[116] Mallat S.Multiefrequencey channel decompositoins on of images and wavelet models.IEEE Trans on Acoustics. Speech,and Signal Processing. 1989, 37(12): 209l-21l0
[117] 崔锦泰著. 小波分析导论. 西安: 西安交通大学出版社. 1997.1
[118] 赵松年,熊小芸著. 子波变换与子波分析. 北京:电子工业出版社. 1997.3
[119] 冉启文编著. 小波分析方法及其应用. 哈尔滨: 哈尔滨工业大学出版社. 1995.12
[120] 丁宇新,沈雪勤等. 基于能量密度的小波神经网络. 计算机学报. 1997, 20(9): 832-838
[121] Mcyer Y.Ondelettes et operateurs.Paris:Hermann Press. 1990. 1-200
[122] 谢美萍.基于小波神经网络的非线性时间序列的逼近. 西北工业大学硕士论文. 1998
[123] F. C. Morabito and A. Gasparics. A Wavelet Neural Network Processor of Eddy Current NDE Data. Electromagnetic Non-Destructive Evaluation III. Proceedings of the 4th International Workshop on E'NDE. Paris, France. 1998 : 108-116
[124] 徐晓霞等. 基于正交最小二乘的小波神经网络. 电子学报. 1998, 26(10): 115-117
[125] Kugarajah T and Zhang Qinghua . Multidimentional wavelet frames. IEEE Trans on NN. 1995, 6(6): 1552-1556
[126] Pati Y C and Krisbnaprasad P S. Analysis and synthesis of feedforward neural network using discrete affine wavelet. IEEE Trans on NN. 1993, 4(1): 73-75
[127] Zhang Qinghua and Benveniste A.Wavelet networks.IEEE Trans On NN. 1992, 3(6): 889-898
[128] Szu H,et al Neural network adaptive wavelets for signal representation and classification. Optical Engineering. 1992, 31(9): 1907-1916
[129] Baskshi B R and Stephanopoubus G.Wavelet:a multiresolution,hierarchical neural network with localized learning.Amer Inist Chem Eng J. 1993, 39(1): 57-81
[130] Boubez T and Peskin R L. Wavelet neural network and receptive field partitioning San Francisco:Proc Of ITCNN'93 (3). 1993, 1544-1549
[131] Zhang Jun,et al .Wavelet neural networks for function learning.IEEE trans on SP, 1995, 43(6): 1485-1497
[132] 石卓尔,焦李成等.子波神经网络. 西安:中国神经网络 1993 年学术大会论文集(上),1993, 85-96
[133] 黄凤岗等. 一种用于自适应的小波神经网络. 电子学报. 1998, 26(8): 143-145
[134] 沈雪勤等. 能量密度在正交小波神经网络中的应用. 1997 年中国神经计算科学大会沦文集南京:(FG)1997, 613-616
[135] 高协平,张 钹. 区间小波神经网络(1)—理沦与实现. 软件学报. 1998, 9(3): 217-221
[136] 高协平,张 钹. 区间小波神经网络(Ⅱ)—性质与模拟. 软什学报. 1998, 9(4): 246-250
[137] 王 岭,焦李成. 区间估计的 FW NN 及其区间学习算法. 电子学报. 1998, 26(4): 41-45
[138] Zhang Liang Jie,LiYanda.Wavelet based fuzzy neural networks Taiwan:Proc Of ISANN'94. 1994
[139] 何振亚等. 用于信号逼近的自适应时延小波小波神经网络. 电子科学学刊. 1998, 20(5): 604-610
[140] 陈维荣等. 一种形式神经元的统一模型. 南京:1997 年中国神经计算科学大会论文集CCNS'97(一). 1997, 78-8l
[141] 李向武,韦岗.小波网络及其结构设计方法.模式识别与人工智能.1997,10: 130-137
[142] Qinghua Zhang. Using Wavelet Network in Nonparametric Estimation. IEEE Trans. on Neural Networks.1997,8 (2) :227-236
[143] J R Hull, et al. A Neural Networks Algorithm using Wavelet and Auto Regressive Inputs for Systems Intentification. Proceedings of the 1997 IEEE International Conference on Neural Networks, 1997, 4 (2) :728-732
[144] S. G. Mallat,Characterization of signals From Multiscales Edges, Computer Science Tech[J], Report, 1991.
[145] S. G. Mallat,Characterization of signals From Multiscales Edges,IEEE Trans. Patt. Anal. Machine Intell[J],1992. 14(7)
[146] 李军,王执锉.小波非线性神经网络的算法研究.东南大学学报. 1995. 17 (5): 259-263
[147] Kreinovich V and Sirisaengtaksin O, et al . Wavelet neural networks are asymptotically optimalapproximators for functions of one variable. Florida: Proc of IEEE ICNN'94. (1), 1994 .299-304
[148] 王士同编著. 神经模糊系统及其应用. 北京: 北京航空航天大学出版社. 1998.6
[149] 刘增良主编. 模糊技术与神经网络技术选编(4). 北京: 北京航空航天大学出版社. 1999.10
[150] 张智星, 孙春在等. 张平安, 高春华等译. 神经-模糊和软计算. 西安: 西安交通大学出版社. 2000.6
[151] 李士勇编著. 模糊控制·神经控制和智能控制论. 哈尔滨: 哈尔滨工业大学出版社. 1996.10
[152] Zadeh L A. Fuzzy sets. Information and Control. 1965. vol. 8. 338-353
[153] Zadeh L A. Fuzzy algorithms. Information and Control. 1968. vol. 12. 94-102
[154] Kosko.B. Fuzzy Associative Memories. In: Kondel A(ed).Fuzzy Expert System Reading. MA: Addison Weley,1987:11-56
[155] L X WANG, MENDEL J M. Fuzzy basis functions, universal approximation, and orthogonal least squares learning[J]. IEEE Trans on Neural Networks, 1992,3(5):807-814
[156] Kosko.B. Fuzzy Associative Memories. In: Kondel A(ed).Fuzzy Expert System Reading. MA: Addison Weley,1987:67-79
[157] Liu P Y. The fuzzy associative memory of max-min fuzzy neural networks with threshold. Fuzzy Sets and Systems, 1998,91(1):45-52
[158] Hayashi Y, Buckley J J, Czogala E.Fuzzy neural networks with fuzzy signals and weights. IEEE Trans. on Neural Networks, 1992,3(5):673-678
[159] Chung F L, Lee T. Fuzzy competitive learning. Neural Networks, 1994,7(4):593-552
[160] Ishibuchi H, Tanaka H. A learning algorithm of fuzzy neural nerworks with triangular fuzzy weights. Fuzzy Sets and Systems, 1995,71(2):277-293
[161] Blanco A, Delgado M, Requena I. Identification of fuzzy relational equations by fuzzy neural networks. Fuzzy Sets and Systems, 1995,71(2):215-226
[162] Blanco A, Delgado M, Requena I. Improved fuzzy neural networks for solving relational equations. Fuzzy Sets and Systems, 1995,72(2):311-322
[163] Wei C J, Yao S S, He Z Y. Evolving max-min neural networks to solve fuzzy relational equations. Fuzzy Sets and Systems, 1995,69(2):258-261
[164] Pedrycz W. Genetic algorithm for learning in fuzzy relational structures. Fuzzy Sets and Systems, 1995,69(1):35-52
[165] Buckley J J, Hayashi Y. Genetic algorithms for fuzzy neural nets. Fuzzy Sets and Systems, 1993,61(1):1-10
[166] B, Kosko, Neural Networks and fuzzy systems: A dynamical systems approach to intelligence, NJ: Prentice Hall, 1992
[167] L.X.Wang, Fuzzy Systems are universal approximator, IEEE Fuzzy' 92, 1992
[168] Buckly J J, Sugeno type controllers are universal controllers, Fuzzy Sets and Systems 1993. 53(3):299-303
[169] ]梁久祯等. 单体模糊神经网络的函数逼近能力[J].计算机研究与进展, 2000, (9).
[170] 赵明洁等. 一类模糊神经网络的函数逼近能力[J].自动化学报, 2000,(2).
[171] Gupta M M et al. Controllability of fuzzy control systems, IEEE Trans. Syst. Man and Cybern. 1986. 16(4):576-582
[172] T Takagi, M Sugeno. Fuzzy identification of system and its applications to modeling and control, IEEE Trans. Syst. Man. and Cybern.1985,(15):116-132
[173] Kazuo Tanaka, M Sugeno. Stability analysis and design of fuzzy control systems, Fuzzy Sets and Systems, 1992, (45):135-156
[174] Won Chul Kim et al. Stability analysis and stabilization of fuzzy state space models, Fuzzy Sets and Systems, 1995, (71):131-142
[175] 陈建勤等.模糊控制系统的闭环模型及稳定性分析[J].自动化学报,1994.20(1):1-10
[176] 陈建勤等.利用关系矩阵分析模糊控制系统的稳定性[J].控制理论与应用,12(5):635-639.
[177] S Kawam oto et al. An approach to stability analysis of second order fuzzy systems, IEEE Internat. Conf. On Fuzzy Systems. 1992,1427-1434
[178] 苗 青,曹广益,朱新坚. 基于自适应模糊神经网络的 DMFC 温度建模和控制. 高技术通讯. 2004.10.66-69
[179] 伦淑娴,张化光. 基于自适应模糊神经网络的噪声抵消器. 仪表技术与传感器. 2003.(5):14-16
[180] 吴一帆. 基于模糊神经网络模式分类的数据挖掘算法 长沙交通学院学报. 2004.20(1).45-49
[181] 王雅娣,曹长修. 模糊 RBF 神经网络在专家系统知识库建立中的应用. 计算机工程. 2005.Vol.31(3):175-177
[182] 曾宪华, 耿新宇, 黄小燕. 基于模糊神经网络的储集层含水饱和度预测. 系统仿真学报 2003.Vol.15(5):735-736
[183] 杨 斌 , 肖 慈 , 王 旬 . 基 于 神 经 模 糊 系 统 的 储 层 参 数 反 演 . 石 油 与 天 然 气 地 质 . 2000.Vol.21(2):173-176
[184] 冉启全, 李士伦. 模糊神经网络及其在储集层油气评价中的应用. 钻采工艺. 1977.Vol.20(1):32-36
[185] 邹开其. 模糊神经网络的若干进展. 浙江万里学院学报.2003.Vol.16(4):54-57
[186] Horikaws S I, et al. On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior. Int. J. Fuzzy sets and systems, Vol. 71, No.1,1995
[187] Harris C J, Moore C G. Phase plane analysis tools for a class of fuzzy control systems. Proceedings of FUUZ-IEEE'92, 1992. 511-518
[188] Hwang G-C, Lin S-C. A stabilities approach to fuzzy control design for nonlinear systems. Fuzzy Sets and Systems, 1992. Vol. 48:279-287
[189] Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and Systems. 1998. vol.28.no.1.15-13
[190] Sugeno M, Tanaka K. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems. 1992.vol.45.no.2.135-156
[191] 尹红军. 试井分析的最优化方法及其应用. 大庆石油学院博士论文. 1999
[192] Holland. J. H. Adaptation in Neural Artificial Systwm the Univ. Of Michigon Press,Michigon. 1975, 10-56
[193] Bageley. J. D. The Behavior of Adaptive System Which Emloy Genetic and Correlation Algorithms .Dissertation Abstracts International. 1976, 28(12): 1231-1240
[194] Hollstien. R. B. Artificial genetic adaptation in computer Control systems (Doctoral dissertation,University of Michigan ). Dissertation Abstracts Internationa. 1971, 2(3).1510B
[195] De Jong K.A, An Analysis of the Behaviour of a Class of Genetic adaptive System:[ph D Dissertation ].University of Michigan. 1975, 76-93
[196] Goldberg .D. E Genetic Algorithms in Search Optimization and Machine Reading. MA: Addison Wesley. 1983
[197] Fitzpartrick,J.N.,Grefenstette,J,J. and Van Gucht,D., Image registration by genetic Algorithms search. Proceedings of IEEE Southeast Conference. 1984, 460-464
[198] Davis,L(Ed.). Handbook of Genetic Algorithms. Van Nostrand Reinhold. New York. 1991
[199] 孙艳丰等. 遗传算法在优化问题中的应用研究进展. 控制与决策. 1996, 11(4): 425-431
[200] 艾丽蓉,何华灿. 遗传算法综述. 计算机应用研究. 1997, 14(4): 3-6
[201] 陈国良. 遗传算法及其应用. 人民邮电出版社. 1996
[202] 刘勇,康立山. 非数值并行算法—遗传算法. 1997
[203] 盂庆春著. 遗传算法及其应用. 山东人学山版社. 1995
[204] 张文修,梁怡编著. 遗传算法的数学基础. 西安交通大学出版社. 2000
[205] Wright A.H. Genetic Algorithms for Real Parameter Optimization . Functions of Genetic Algorithms. San Matei, CA:Morgan Kaufmann. 1991, 205-218
[206] Qi X.,Pal-Mieri F. Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part I: Basic properties of Selection and Mutation .IEEE Trans.on Neural network. 1994, 5(1): 102-129
[207] Goldberg D.E.,Segreat.P. Finite Markov chain Analysis of Genetic Algorithm. Proceedings of the 2nd International Convergence on Gas. Massachusette institute of Technology Cambridge ,MA. 1987,1-8
[208] Eiben A.E, et.al .Global Convergence of Genetic Algorithms: An Inifinite Markov Chain Analysis. Parallel Problem Solving From Nature .Berlin: Spring-Verlg . 1991, 4-12
[209] Rudolph G. Convergence Analysis of Genetic algorithms. IEEE Trans .On Neural network . 1994, 5(1): 96-101
[210] Grefenstette J.J. GA for The traveling Salesman Problem .Proc. of int.Con.on.GA and their Appl .Lawrence Earlbaum. 1985, 160-168
[211] Ludmila. I , F . Kuncheva . Fitness Functions in Editing k-NN Reference Set by Genetic Algorithms.Pattern Recognition. 1997, 30(6): 1042-1049
[212] 刘宝坤,石江瑞,王慧,赵丛. 基于遗传算法的神经网络自适应控制器的研究. 信息与控制. 1997, 26(4): 311-314
[213] 黄诗峰 等,地质统计学中变差函数参数估计的新方法,地质与勘探,1999,1. 41-43
[214] Steve. A ,Billings,and Guang L.zheng.Radial Basis Function Network Configuration Using Genetic A1gorlthms.Neural Network. 1995, 8(6): 877-890
[215] Nello Cristianini. John Shawe-Taylor. 支持向量机导论. 李国正,王猛等译. 北京: 电子工业出版社. 2005.1
[216] Vapnik. V. The Nature of Statistical Learning Theory.Springer Verlag,1995.
[217] Vapnik. V. Statistical Learning Theory.Wiley,1998.
[218] Duda, R.O. and P.E. Hart. Patter Classification and Scene Analysis. Wiley. 1973
[219] Cover, T.M. Geometrical and statistical properties of systems of linear nequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 1965. 14:326-334
[220] Vapnik, V. and A. Lerner. Pattern recognition using generalized portrait method. 1963. Automation and Remote Control, 24.
[221] Anlauf, J.K. and M. Biehl . The adatron: an adaptive perceptron algorithm. Europhysics Letters, 1989. 10:687-692
[222] Aronszajn, N. Theory of reproducing kernels. Transactions of the American athematical Society, 1950.68:337-404
[223] Poggio, T. On optimal nonlinear associative recall. Biological Cybernetics, 1975. 19:201 -209
[224] Aizerman, M., E. Braverman, and L. Rozonoer. Theoretical foundations of the potent -ial function method in pattern recognition learning. Automation and Remote Control .1964. 25:821-837
[225] Mangasarian, O. L. Linear and nonlinear separation of patterns by linear programming. Operations Research, 1965.13:444--452
[226] Smith, F.W. Pattern classifier design by linear programming. IEEE Transactions on Computers, 1968. C-17:367-372
[227] Mangasarian, O. L. Multi-surface method of pattern separation. IEEE Transactions onInformation Theory , 1968. IT-14:801-807
[228] Boser, B.E, I.M. Guyon and V. Vapnik. A Training Algorithm for Optimal Margin Classifiers, Fifth Annual Workshop on Computational Learning Theory, ACM. 1992
[229] Anthony, M. and P. Bartlett. Learning in Neural Networks : Theoretical Foundations. Cambridge University Press. 1999
[230] Cristianini, N. and Shawe-Taylor, J. AN INTRODUCTION TO SUPPORT VECTOR MACHINES (and other kernel-based learning methods). Cambridge University Press 2000
[231] Wakamatsu, Y, M. Mochii, K.S. Vogel, and J.A. Weston. Avian neural crest-derived neurogenic precursors undergo apoptosis on the lateral migration pathway. Development. 1998. 125, 4205-4213
[232] Smola, A.J. and B. Sch?lkopf . A Tutorial on Support Vector Regression. Produced as part of the ESPRIT Working Group in Neural and Computation Learning , NeuroCOLT2. Technical Report Series NC2-TR-1998-030, October 1998
[233] Schoelkopf, B., C.J.C. Burges, and A.J. Smola. Advances in kernel methods – support vector learning, MIT Press, Cambridge, MA. 1999
[234] Tikhonov, A. N. Solution of incorrectly formulated problems and the regularization method. Soviet Math. Dokl., 1963. 4:1035-1038
[235] Poggio, T. and F. Girosi. Regularization Algorighms for Learning That Are Equivalent to Multilayer Networks. Science. 1990. 247:pp. 978-982
[236] Smola, A.J., P. Bartlett, B. Schoelkopf, and C. Schuurmans. Advances in large margin classifiers, MIT Press, Cambridge, MA. 1999
[237] Smola, A. J. Learning with kernel. Ph.D Thesis, 1998
[238] Mangasarian O.L. and D. R. Musicant. Nonlinear Data Discrimination via Generalized Support Vector Machines ICCP99: International Conference on Complementarity Problems,Madison, Wisconsin, 1999. June 9-12, 19.
[239] Cortes C. and V. Vapnik. Support vector networks. Machine Learning, 1995. vol.20, pp. 273 –297
[240] Osuna, E, R. Freund, F. Girosi. An improved training algorithm for support vector machines, Proc. IEEE Neural Netw For sign. Proc. 1997. NNSP 97, 276-285
[241] Joachims, T, Making Large-Scale Support Vector Machines Learning Practical. In Advances in Kernel Methods, 169-184, 1998
[242] Platt, J.C. Sequential Min imal Optimisation: a fast algorithm for training support vector machines, Techn. Rep. MSR-TR-98-14, Microsoft Research. 1998
[243] Cauwenberghs, G. and T. Poggio. Incremental and decremental support vector mach -ine learning. Advances in Neural Information Processing Systems (NIPS*2000) 2001,13.
[244] Ralaivola L. Florence d'Alché-Buc: Incremental Support Vector Machine Learning: A Local Approach. ICANN 2001: 322-330
[245] Suykens, J.A.K. Nonlinear Modelling and Support Vector Machine, IEEE Instrument -ation and Measurement Technology Conference Budapest, Hungary, 2001.May 21-23
[246] Carozza, M. and S. Rampone. Towards an incremental SVM for regression. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy 2000, 6: 405-410
[247] Xiao Rong , Wang Jicheng and Zhang Fayan. An approach to incremental SVM learning algorithm. Proceedings of 12th IEEE International Conference on Tools with Artificial Intelligence, ancouver, BC, Canada, 2000: 268-273
[248] Vapnik, V. Principles of Risk Minimization for Learning Theory. 1991. NIPS 831-838.
[249] 张学工. 关于统计学习理论与支持向量机. 自动化学报.26(1). 2000
[250] Hearst M.A, B. Scholkopf and Dumais Setal. Trends and controversies-support vector machines. IEEE Intelligent Systems, 1998, 13 (4): 18-28
[251] Keren, D, M. Osadchy and C. Gotsman. Antifaces: A novel, fast method for image detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23 (7):747-761
[252] Vailaya A, H. Zhang, Zhang HongJiang et al. Automatic image orientation detection. IEEE Transactions on Image Processing, 2002, 11(7): 746 –755
[253] Guo Guo-Dong A.K.Jain, Ma Wei-Ying et al. Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks, 2002, 13 (4): 811-820
[254] Brown, M, H.G. Lewis, S.R. Gunn. Linear spectral mixture models and support vector machines for remote sensing, (submitted to)IEEE Trans. Geoscience and Remote Sensing. 2000
[255] Xin Dong and Wi Zhaohui. Speaker recognition using continuous density support vector machines. Electronics Letters, 2001, 37 (17-16): 1099-1101
[256] Bahlmann C., B. Haasdonk and H. Burkhardt. On-line handwriting recognition with support vector machines -a kernel approach. Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, 2002: 49-54
[257] Kim K.I, J. Kim and K. Jung. Recognition of facial images using support vector machine. Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001: 468-471, Singapore.
[258] Drezet P.M.L. and R.F. Harrison. Support vector machines for system identification. UKACC International Conference on Control '98, 1998, 1 (1):688-692
[259] De Kruif B.J. and T.J.A. de Vries. On using a support vector machine in learning feed-forward control. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2001, 1 (1):272-277
[260] Rychetsky M., S. Ortmann and M. Glesner. Support Vector Approaches for Engine Knock Detection. International Joint Conference on Neural Networks (IJCNN 99), 1999, Washington, USA.
[261] Gao Junfeng, Shi Wengang, Tan Jianxun et al. Support vector machines based approach for fault diagnosis of valves in reciprocating pumps. Proceedings of IEEE Conference on Electrical and Computer Engineering, 2002, 3.1622 -1627 Canadian.
[262] J.Weston and C.Watkins. Support vector machines for multi-class pattern recognition. In Proceeding of the 6th European symposium on Artificial Neural Network (ESANN),1999
[263] J.Platt,N.Cristianini,and J.Shawe-Taylor. Large margin DAGs for multiclass classification. In Neural Information Processing System (NIPS99),1999.to appear.
[264] Zhang Qinghua.. Regressor selection and wavelet network construction. INRIA Technical Report. No.709-719
[265] 张邦礼,李银国等.小波神经网络的构造及其算法的鲁棒性分析.重庆大学学报. 1995, 18(6): 88-95
[266] 李银国,张邦礼等.小波神经网络及其结构设计方法.模式识别与人工智能. 1997, 9(3): 197-205
[267] 吴耀军等. B 样条小波神经网络.模式识别与人工智能.1996,9(3):22-233
[268] 水鹏朗,保 铮,焦李成. 一种基于子波神经网络的多尺度差分方程求解新方法. 电子科学学刊.1997, 19(6): 733-737
[269] Christopher J. C.Burges. A tutorial on support vector machine for pattern recognition. Data Mining and knowledge discovery. Vol.2,no.2. 1998
[270] 李建平编. 小波分析与信号处理—理论、应用及软件实现. 重庆出版社. 1997
[271] Song Xianyu, et al . Adaptive Wavelet neural network for pattern recognition. Nanjing: Procof IEEE ICNNSP '95. 1995, 386-389
[272] 柳振宇等. 基于不变矩和子波神经网络的图象识别. 武汉: 神经网络及应用—'94 最新进展,. 1994, 552-555
[273] 李向武,韦岗. 小波神经网络的动态系统辨识方法及应用. 控制理论与应用. 1998, 15(4) : 494-500
[274] 金龙等. 基于小波神经网络的长期预报研究. 南 京: CCNS'97 (二). 1997, 617-620
[275] 李建平等. 基于小波变换和神经网络的图象压缩. 成都: 神经网络理论与应用研究 '96. 1996, 436-430
[276] LiuWei, et al. The compression of Irspectra by using wavelet neural network: Chinese Science Bulletin. 1997, 10: 822-825
[2] Kohonen, T. K. Self-Organizayion and Associative Memory. New York.Spring-Verlag. 1988
[3] Rumelhart, D. E, and McCIelland (eds) . Parallel Distributed Processing, volume 1 and Volume 2. MIT Press,Cambridge. 1986
[4] Rosenblatt, F. Principles of Neurodynamics. Spartan New York. 1962
[5] Hinton, J. E. T. J. Sejnowski. Learning and Relearning in Boltzmann Machine MIT Press. Cambridge MA. 1986
[6] Hebb, D.D, The Organization of Behavior. John Wiley & Sons. New York. 1949
[7] Widrow, B. M. E. Hoff. WESCON Convention Record. 1960, 4: 99-104
[8] Minsky, M. and S. Papert, Perception: An Introduction to Computation Geometry.Mit Press, Cambridge. Massachusetts. 1969
[9] T.Kohonen, Associative Memory :A System Theory Approach. Springer. New York. 1977
[10] T.Kohonen, Cognitron : A Self-Organization Multilayered Neural network. Biol. Cyber. 1975, (20): 121-136
[11] Widrow. B. S.D.stearns, Adaptive Signal Processing. Prencetice-Hall. Englewood Cliffs, NJ. 1985
[12] Marlsburg. Cvonder. Self-Organization of Orientation Sensitive Cell in the Striated Cortex. Kybernetic. 1973, (14): 85-100
[13] Grossberg.S. Adaptive .Pattern Classification and Universal Recording. Boil. Cybernet. 1976, 23: 121-134
[14] Fukushima, K. A Self-Organizing Multilayered Neural Networks.Boil. Cybernet. 1975, 20: 121-136
[15] Hopfild, J. J. Neural network and physical systems with emergent collective computational abilities. Proc .Natl.Acad.Sci.USA. 1982, 79: 1552-2558
[16] DAPRA Neural Network Study . AFCEA international Press. 1988
[17] K.Fukushima, Neocognition: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cyber. 1980, 36(4): 193-202
[18] K.Fukushima and S.Miyake, Necognitron :A New Algorithm for Pattern Recognition Tolerant of Deformation and Shifts in Position.Pattern Recognition. 1982, 15(6): 455-469
[19] F.L. Lewis, K. Liu, and A. Yesildirek. Neural net robot controller with guaranteed tracking Performance. IEEE Trans. Neural Networks. 1995, 703-710
[20] Ahmed Tarraf and Martin Meyers;Neural Network-Based Voice Quality Measurement Technique. Proceedings of the The Fourth IEEE Symposium on Computers and Communications. 1998
[21] S. McLoone, M.D. Brown, G. Irwin and G. Lightbody. A Hybrid Linear/Nonlinear training Algorithm for Feedforward Neural Networks. IEEE Transactions on Neural Networks. 1998, 9(9): 669-683
[22] K.S. Narendra, Adaptive Control Using Neural Networks. Neural Networks for Control. MIT Press. 1991, 115-142
[23] L.G. Kraft and D.P. Campagna, A Summary Comparison of CMAC Neural Networks and Traditional Adaptive Control Systems. Neural Networks and Control. MIT Press. 1991, 143-169
[24] S. Jagannathan and F.L. Lewis. Multilayer discrete-time neural net controller with guaranteed performance. IEEE Trans. Neural Networks. to appear. 1995
[25] M.S. Dawson, A.K. Fung, and M.T. Manry. Surface Parameter Retrieval Using Fast Learning Neural Networks. Remote Sensing Reviews. 1993, 7: 1-18
[26] X. Jiang, Mu-Song Chen, M. T. Manry, M.S. Dawson, A.K. Fung. Analysis an Optimization of Neural Networks for Remote Sensing. Remote Sensing Reviews. 1994, 9: 97-114
[27] M.T. Manry, S.J. Apollo, L.S. Allen, W.D. Lyle, W. Gong, M.S. Dawson, and A.K. Fung. Fast Training of Neural Networks for Remote Sensing. Remote Sensing Reviews. 1994, 9: 77-96
[28] A. Khotanzad, R-C Hwang, and D. Maratukulam. Hourly Load Forecasting by Neural Networks. IEEE PES Winter Meeting. Columbus Ohio. February. 1993
[29] 中国地质学会数学地质专业委员会编. 中国数学地质. 1998,8
[30] 陆文凯,李衍达,谢军等. 地层真电阻率恢复神经网络法. 测井技术. 1999,23(1): 19-23
[31] 程方道. 神经网络在地球物理反演中的应用. 地球物理学进展. 1994, 9(3): 51-59
[32] 陆文凯,李衍达等. 误差反向传播神经网络法地震反演. 地球物理学报. 1996, 39: 293-310
[33] 焦李成编著. 神经网络的应用与实现. 西安:西安电子工业出版社. 1996
[34] 焦李成著. 神经网络系统理论. 西安:西安电子科技大学出版社. 1990
[35] 赵振宇,徐用懋著. 模糊理论和神经网络的基础和应用. 北京:清华大学出版社. 1996
[36] 刘震编著.储层地震地层学. 北京:地质出版社. 1997.
[37] 袁秉衡主编. 应用地震技术研究储层. 北京: 石油工业出版社. 1992.
[38] 于兴河,李剑峰. 碎屑岩系储层地质建模及计算机模拟.北京:地质出版社,1996.
[39] 陈恭洋. 碎屑岩储层随机建模. 北京:地质出版社,2000.
[40] 邓宏文,王红亮,祝永军著. 高分辨率层序地层学原理及应用.北京:地质出版社,2002
[41] 曹建福,韩崇昭,方洋旺. 非线性系统及应用[M]. 西安:西安交通大学出版社,2001
[42] 李正文,李琼,吴朝容. 沉积盆地有效储集层综合识别技术[M]. 成都:四川科技出版社,2002, 44-57
[43] 邱家骧,陶奎元,赵俊磊,等编著.火山岩[M].北京:地质出版社, 1996. 1-58
[44] 陶奎元. 火山岩相构造学[M] . 南京:江苏科技出版社,1994. 20-551
[45] 李双成,郑度. 人工神经网络模型在地学研究中的应用进展. 地球科学进展.2003,18(1)
[46] 刘宪斌, 林金逞. 地震储层研究的现状及展望. 地球学报. 2002, 23(1):73-78
[47] 侯贵卿, 孙萍. 油气储层研究新动向. 海洋石油. 2000. Vol.103(1):8-14
[48] 吴朝容,李正文. 地震多参数油气预测方法及应用研究. 沉积与特提斯地质.2002,22(4).
[49] 冉建斌,李建雄. 基于三维地震资料的油藏描述技术和方法. 石油地球物理勘探.2004,39(1).
[50] 许 多 , 李 显 贵 , 刘 殊 等 . 合 兴 场 地 震 资 料 吸 收 油 气 预 测 研 究 [J]. 物 探 化 探 计 算 技术,2000 ,22(1) :56-58
[51] 王斌,李显贵,文雪康. 动态时窗多参数油气预测技术在柏桠的应用[J]. 物探化探计算技术,2000 ,22(1) : 179-183
[52] 冉建斌. 精细油藏描述技术在油藏滚动勘探开发中的应用. 勘探家,2001 ,4.
[53] 吕晓光,杜庆龙. 应用人工神经网络模型进行孔隙度和渗透率参数预测[J]. 大庆石油地质与开发,1996 ,15 (3) :27-31
[54] 蒋建平, 康贤. 层物性参数展布的相控模型[J].成都理工学院学, 1995,22 (1):12-17
[55] 邱家骧. 国际地科联火成岩分类学分委会推荐的火山岩分类简介[J]. 现代地质, 1991 , 5 (4) :457-468
[56] 王永刚等.利用地震信息预测储层裂缝发育带.石油物探, 2000, 39
[57] 赵海玲. 火山岩储层. 现代地质,1998,12(1):62
[58] 李长山, 陈建文. 火山岩储层建模初探. 地学前缘. 2000, Vol.7(4):381-388
[59] 李 明 , 邹 才 能 . 松 辽 盆 地 北 部 深 层 火 山 岩 气 藏 识 别 与 预 测 技 术 . 石 油 地 球 物 理 勘探.2002,37(5):477-484
[60] 刘启 , 舒萍 . 松辽盆地北部深层火山岩气藏综合描述技术 . 大庆石油地质与开发 . 2005,24(3):21-23
[61] 何 琰, 伍友佳. 火山岩油气藏研究[J]. 大庆石油地质与开发, 1999, 18 (4) : 6-8
[62] 张晓东, 霍 岩. 松辽盆地北部地区火山岩特征及分布规律[J]. 大庆石油地质与开发, 2000, 19 (4): 10-12
[63] Gardner M W. Dorling S R. Artificial neural networks (multilayer perceptron) —A review of applications in the atmospheric sciences[J ]. Atmospheric Environment,1998,32 (14～15):2627-2636
[64] Bahorich M and Farmer S. 3-D seismic discontinuity for faults and stratigraphic features : The coherence cube [ J ] . The Leading Edge , 1995. 14 (10) :1053-1058
[65] Chen Q , Sideney S. Seismic attribute technology for reservoir forecasting and monitoring [J] . The Leading Edge , 1997.16 ( 5) : 445-456
[66] Jan Helgesen. Comparison of constrained sparse spike and stochastic inversion for porosity prediction at Kristin Field. The Leading Edge ,2000 (4).
[67] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties ,part 1 :A da2ta interpretation methodology. T EL ,1994 ,13 (5) :305-310
[68] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties ,Part 2 :Using artificial neural networks for nonlinear attribute calibra tion1 T EL ,1994 ,13 (5) :674-678
[69] Philip S Shuki Ronen ,Masami Hattori , Chip Corbett .Seismic-guided estimation of log properties , Part 3 : A controlled study1 T EL ,1994 , 13 (5) :770-776
[70] Kurt J M 1 3D seismic at tributes using a semblancebased coherency algorithm. Geophysics, 1998, 63.
[71] Levin L E. Volcanogenic and volcaniclastic reservoir rocks inMesozoic -Cenozoic island arcs: examp les from the Caucasus and the NW Pacific [ J ] 1Journal of Petroleum Geology, 1995, 18 (3 ) : 267-288
[72] 边肇祺, 张学工编著. 模式识别(第二版)[M]. 北京: 清华大学出版社. 2000.
[73] 傅京孙主编. 模式识别应用. 北京大学出版社. 1990.
[74] 殷勤业等编译. 模式识别与神经网络. 机械工业出版社. 1992
[75] 熊承义, 李玉海. 统计模式识别极其发展现状综述. 科技进步与对策. 2003. 8. 173-175
[76] Zadeh, L. A. Fuzzy sets and their application to classification and clustering[M]. New York: Academic Press, 1977. 251-299
[77] Timothy J. Ross. Fuzzy logic with engineering applications[M]. New York: McGraw-Hill, 1995
[78] Paul S. Wu, Ming Li. Supervised and unsupervised fuzzy-adaptive Hamming net[J]. Patern Recognition,2002,32:1801-1816
[79] F.Behloul, B.PF.Lelieveldt, A.Boudraa, et al. Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data[J]. Patern Recognition,2002,35:659-675
[80] 卢涛, 陈德钊. 径向基网络的研究进展和评述. 计算机工程与应用. 2005.4:60-62
[81] 焦李成, 陈德钊. 基函数网络逼近:进展与展望. 工程数学学报. 2002.19(1):21-36
[82] 周峻武, 孙传荛等. 径向基函数网络(RBF)的研究及实现[J]. 矿冶. 2001.10(4):71-75
[83] 柴 杰 , 江 青 茵 等 . RBF 神 经 网 络 的 函 数 能 力 及 其 算 法 [J]. 模 式 识 别 与 人 工 智 能 . 2002.15(3):310-316
[84] Moody J, Darken C. Learning with the localized receptive fields. In: Proc, Connectionist Models Summer School, Camegie Mellon Univ. 1988
[85] Moody J, Darken C. Fast Learning in Networks of Locally-luned Processing Units[J]. Neural Computation,1989:I(1):281-294
[86] Powell M J D. Radial basic function approximations to polynomials[C]. In:12th Biennial Numerical Analysis Conf(Dundee),1987:223-241
[87] H. N. Mhaskar, C. A. Micchelli Approximation by Superposition of Sigmoidal and Radial Functions. Advances on Applied Mathematics, 1992, (13):350-373
[88] T. Chen, H. Chen and R. W. Liu Approximation Capacity in C(R") by Multi-layer. Feeforward Networks and Related Problems. IEEE Trans. On Neural Networks, 1995, 6(1): 185-198
[89] Moshe Leshno Multilayer Feedforward Networks with a Non-polynomial Activation Function Can Approximate Any Function. Neural Networks, 1993, 6: 861-867
[90] Chen TianPing, Chen Hong Approximation Theory Capability to Functions of Several Variables, Nonlinear Functions, and Operators by Radial Basis Functional Neural Networks. IEEE Trans. on Neural Networks, 1995, 6(4):904-910
[91] Broomhead, D. S., Lowe, D. Multivariable functional interpolation and adaptive networks. Complex systems, vol.2, pp: 321-335
[92] 张玉瑞,陈剑波. 基于 RBF 神经网络的时间序列预测.计算机工程与应用. 2005.11.74-76
[93] 姬晓飞,申东日,陈义俊. 基于改进的 RBF 神经网络在线辨识算法及其应用. 计算机仿真. 2003.Vol.20(11):61-63
[94] Sanner, R. M., Slotine, J.-J. E., (1994). Gaussian networks for direct adaptive control. IEEE Trans. On Neural networks, vol. 2, no. 6, pp: 837-863
[95] Darken C, Moody J. Fast adaptive K-mean clustering:some empirical results[C]. In: Proceeding International Joint Conference on Neural Networks, Piscataway: IEEE, Press,1990:238
[96] Lei Xu. Rival Pennalized Competitive Learning for Clustering Analysis, RBF Networks and Curve Dectection[J]. IEEE Trans on Neural Networks, 1993,4(4):636-649
[97] Chen S, Grant P M, Cowan C F N. Orthogonal Least Squares Learning Algorithm for training Multioutput Radial Basis Function Networks. IEEE Proceedings F,1992,139(6):378-384
[98] Chen S, Billings S A, Grant P M. Rescursive Hybrid Algorithm for Nonlinear Identification Using Radial Basis Function Networks[J]. International Journal of Control,1995,55(5):1051-1070
[99] B. Walezak, D L Massart. The Radial Basis Function-Partial Least Squares Approach as a Flexible Non-linear Regression Technique[J]. Analytical Chimical Acta,1996, 331:177-185
[100] Lang P M, Brencheley J M, Nieves R G et al. Cyclic Subspace Regression[J]. Journal of Multivariate Analysis, 1998,65:58-70
[101] Goldberg D E. Genetic algorithms in Search, optimization and machine reading[M]. MA:Addison-Wesley,1989
[102] Orvosh D, Davis L. Using Algorithm to Optimize Problems with Feasibility Constraints[C]. In:Proc of 1st IEEE Cont on Evolutionary Computation, 1994:548-553
[103] Chen H, Flann N S. Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybird Methods. In: Parallel Problem Solving from Nature 3, Springer –Verlag,1994:428-438
[104] Robert J , Schilling James , Carroll J . Approximation of nonlinear systems with radial basis function neural networks[J] . IEEE TRANSACTIONS ON NEURAL NETWORKS , 2001 , 12 (1) : 21-28
[105] Mukherjee S and Nayar S K.Automatic Generation of RBF Network Suing Wavelet. Pattern Recognition 2nd. 1996, 29(8): 1369-1383
[106] Chui C K.An introduction to Wavelets.New York:Academic Press. 1992
[107] Chui C K. Wavelet: a tutorial theory and applications. New York: Academic Press. 1992,1-432
[108] Daubechies I .Ten Lectures on Wavelets. Philadelphia. PA:SIAM Press. 1992
[109] Baskstlg R and Stephanopoubus C. Wavelet:a multiresolution,hierarchical neural network with localized learning.Amer Inist Chem Eng J. 1993, 39(1): 57-81
[110] Young R.An introduction to nonlinear Fourier series. New York,Academic Press. 1980: 1-150
[111] Roberto Kawakami Harrop Galvao and Takashi Yoneyama. A Neural Classifier Employing Biased Wavelets. Proceedings of the Vth Brazilian Symposium on Neural Networks. 1998
[112] Morlet.J. Wave Propation and Sampling theory and complex waves.Geophysics.1982, 47(2): 222-236
[113] Daubcchles .I. Orthonormal bases of compactly supported wavelets.Comm.on Pure and Appl.Math . 1988, 41(7): 909-996
[114] Burt P J,Adelson E.The Laplacian Pyramid as a compact image code.IEEE Trans on Communicatlons. 1983, 31(4): 532-540
[115] Mallat. S.A theory for multiresolutions signal decomposition :the wavelet representation.IEEE Trans.on PAMI. 1989, 1l(7): 674-693
[116] Mallat S.Multiefrequencey channel decompositoins on of images and wavelet models.IEEE Trans on Acoustics. Speech,and Signal Processing. 1989, 37(12): 209l-21l0
[117] 崔锦泰著. 小波分析导论. 西安: 西安交通大学出版社. 1997.1
[118] 赵松年,熊小芸著. 子波变换与子波分析. 北京:电子工业出版社. 1997.3
[119] 冉启文编著. 小波分析方法及其应用. 哈尔滨: 哈尔滨工业大学出版社. 1995.12
[120] 丁宇新,沈雪勤等. 基于能量密度的小波神经网络. 计算机学报. 1997, 20(9): 832-838
[121] Mcyer Y.Ondelettes et operateurs.Paris:Hermann Press. 1990. 1-200
[122] 谢美萍.基于小波神经网络的非线性时间序列的逼近. 西北工业大学硕士论文. 1998
[123] F. C. Morabito and A. Gasparics. A Wavelet Neural Network Processor of Eddy Current NDE Data. Electromagnetic Non-Destructive Evaluation III. Proceedings of the 4th International Workshop on E'NDE. Paris, France. 1998 : 108-116
[124] 徐晓霞等. 基于正交最小二乘的小波神经网络. 电子学报. 1998, 26(10): 115-117
[125] Kugarajah T and Zhang Qinghua . Multidimentional wavelet frames. IEEE Trans on NN. 1995, 6(6): 1552-1556
[126] Pati Y C and Krisbnaprasad P S. Analysis and synthesis of feedforward neural network using discrete affine wavelet. IEEE Trans on NN. 1993, 4(1): 73-75
[127] Zhang Qinghua and Benveniste A.Wavelet networks.IEEE Trans On NN. 1992, 3(6): 889-898
[128] Szu H,et al Neural network adaptive wavelets for signal representation and classification. Optical Engineering. 1992, 31(9): 1907-1916
[129] Baskshi B R and Stephanopoubus G.Wavelet:a multiresolution,hierarchical neural network with localized learning.Amer Inist Chem Eng J. 1993, 39(1): 57-81
[130] Boubez T and Peskin R L. Wavelet neural network and receptive field partitioning San Francisco:Proc Of ITCNN'93 (3). 1993, 1544-1549
[131] Zhang Jun,et al .Wavelet neural networks for function learning.IEEE trans on SP, 1995, 43(6): 1485-1497
[132] 石卓尔,焦李成等.子波神经网络. 西安:中国神经网络 1993 年学术大会论文集(上),1993, 85-96
[133] 黄凤岗等. 一种用于自适应的小波神经网络. 电子学报. 1998, 26(8): 143-145
[134] 沈雪勤等. 能量密度在正交小波神经网络中的应用. 1997 年中国神经计算科学大会沦文集南京:(FG)1997, 613-616
[135] 高协平,张 钹. 区间小波神经网络(1)—理沦与实现. 软件学报. 1998, 9(3): 217-221
[136] 高协平,张 钹. 区间小波神经网络(Ⅱ)—性质与模拟. 软什学报. 1998, 9(4): 246-250
[137] 王 岭,焦李成. 区间估计的 FW NN 及其区间学习算法. 电子学报. 1998, 26(4): 41-45
[138] Zhang Liang Jie,LiYanda.Wavelet based fuzzy neural networks Taiwan:Proc Of ISANN'94. 1994
[139] 何振亚等. 用于信号逼近的自适应时延小波小波神经网络. 电子科学学刊. 1998, 20(5): 604-610
[140] 陈维荣等. 一种形式神经元的统一模型. 南京:1997 年中国神经计算科学大会论文集CCNS'97(一). 1997, 78-8l
[141] 李向武,韦岗.小波网络及其结构设计方法.模式识别与人工智能.1997,10: 130-137
[142] Qinghua Zhang. Using Wavelet Network in Nonparametric Estimation. IEEE Trans. on Neural Networks.1997,8 (2) :227-236
[143] J R Hull, et al. A Neural Networks Algorithm using Wavelet and Auto Regressive Inputs for Systems Intentification. Proceedings of the 1997 IEEE International Conference on Neural Networks, 1997, 4 (2) :728-732
[144] S. G. Mallat,Characterization of signals From Multiscales Edges, Computer Science Tech[J], Report, 1991.
[145] S. G. Mallat,Characterization of signals From Multiscales Edges,IEEE Trans. Patt. Anal. Machine Intell[J],1992. 14(7)
[146] 李军,王执锉.小波非线性神经网络的算法研究.东南大学学报. 1995. 17 (5): 259-263
[147] Kreinovich V and Sirisaengtaksin O, et al . Wavelet neural networks are asymptotically optimalapproximators for functions of one variable. Florida: Proc of IEEE ICNN'94. (1), 1994 .299-304
[148] 王士同编著. 神经模糊系统及其应用. 北京: 北京航空航天大学出版社. 1998.6
[149] 刘增良主编. 模糊技术与神经网络技术选编(4). 北京: 北京航空航天大学出版社. 1999.10
[150] 张智星, 孙春在等. 张平安, 高春华等译. 神经-模糊和软计算. 西安: 西安交通大学出版社. 2000.6
[151] 李士勇编著. 模糊控制·神经控制和智能控制论. 哈尔滨: 哈尔滨工业大学出版社. 1996.10
[152] Zadeh L A. Fuzzy sets. Information and Control. 1965. vol. 8. 338-353
[153] Zadeh L A. Fuzzy algorithms. Information and Control. 1968. vol. 12. 94-102
[154] Kosko.B. Fuzzy Associative Memories. In: Kondel A(ed).Fuzzy Expert System Reading. MA: Addison Weley,1987:11-56
[155] L X WANG, MENDEL J M. Fuzzy basis functions, universal approximation, and orthogonal least squares learning[J]. IEEE Trans on Neural Networks, 1992,3(5):807-814
[156] Kosko.B. Fuzzy Associative Memories. In: Kondel A(ed).Fuzzy Expert System Reading. MA: Addison Weley,1987:67-79
[157] Liu P Y. The fuzzy associative memory of max-min fuzzy neural networks with threshold. Fuzzy Sets and Systems, 1998,91(1):45-52
[158] Hayashi Y, Buckley J J, Czogala E.Fuzzy neural networks with fuzzy signals and weights. IEEE Trans. on Neural Networks, 1992,3(5):673-678
[159] Chung F L, Lee T. Fuzzy competitive learning. Neural Networks, 1994,7(4):593-552
[160] Ishibuchi H, Tanaka H. A learning algorithm of fuzzy neural nerworks with triangular fuzzy weights. Fuzzy Sets and Systems, 1995,71(2):277-293
[161] Blanco A, Delgado M, Requena I. Identification of fuzzy relational equations by fuzzy neural networks. Fuzzy Sets and Systems, 1995,71(2):215-226
[162] Blanco A, Delgado M, Requena I. Improved fuzzy neural networks for solving relational equations. Fuzzy Sets and Systems, 1995,72(2):311-322
[163] Wei C J, Yao S S, He Z Y. Evolving max-min neural networks to solve fuzzy relational equations. Fuzzy Sets and Systems, 1995,69(2):258-261
[164] Pedrycz W. Genetic algorithm for learning in fuzzy relational structures. Fuzzy Sets and Systems, 1995,69(1):35-52
[165] Buckley J J, Hayashi Y. Genetic algorithms for fuzzy neural nets. Fuzzy Sets and Systems, 1993,61(1):1-10
[166] B, Kosko, Neural Networks and fuzzy systems: A dynamical systems approach to intelligence, NJ: Prentice Hall, 1992
[167] L.X.Wang, Fuzzy Systems are universal approximator, IEEE Fuzzy' 92, 1992
[168] Buckly J J, Sugeno type controllers are universal controllers, Fuzzy Sets and Systems 1993. 53(3):299-303
[169] ]梁久祯等. 单体模糊神经网络的函数逼近能力[J].计算机研究与进展, 2000, (9).
[170] 赵明洁等. 一类模糊神经网络的函数逼近能力[J].自动化学报, 2000,(2).
[171] Gupta M M et al. Controllability of fuzzy control systems, IEEE Trans. Syst. Man and Cybern. 1986. 16(4):576-582
[172] T Takagi, M Sugeno. Fuzzy identification of system and its applications to modeling and control, IEEE Trans. Syst. Man. and Cybern.1985,(15):116-132
[173] Kazuo Tanaka, M Sugeno. Stability analysis and design of fuzzy control systems, Fuzzy Sets and Systems, 1992, (45):135-156
[174] Won Chul Kim et al. Stability analysis and stabilization of fuzzy state space models, Fuzzy Sets and Systems, 1995, (71):131-142
[175] 陈建勤等.模糊控制系统的闭环模型及稳定性分析[J].自动化学报,1994.20(1):1-10
[176] 陈建勤等.利用关系矩阵分析模糊控制系统的稳定性[J].控制理论与应用,12(5):635-639.
[177] S Kawam oto et al. An approach to stability analysis of second order fuzzy systems, IEEE Internat. Conf. On Fuzzy Systems. 1992,1427-1434
[178] 苗 青,曹广益,朱新坚. 基于自适应模糊神经网络的 DMFC 温度建模和控制. 高技术通讯. 2004.10.66-69
[179] 伦淑娴,张化光. 基于自适应模糊神经网络的噪声抵消器. 仪表技术与传感器. 2003.(5):14-16
[180] 吴一帆. 基于模糊神经网络模式分类的数据挖掘算法 长沙交通学院学报. 2004.20(1).45-49
[181] 王雅娣,曹长修. 模糊 RBF 神经网络在专家系统知识库建立中的应用. 计算机工程. 2005.Vol.31(3):175-177
[182] 曾宪华, 耿新宇, 黄小燕. 基于模糊神经网络的储集层含水饱和度预测. 系统仿真学报 2003.Vol.15(5):735-736
[183] 杨 斌 , 肖 慈 , 王 旬 . 基 于 神 经 模 糊 系 统 的 储 层 参 数 反 演 . 石 油 与 天 然 气 地 质 . 2000.Vol.21(2):173-176
[184] 冉启全, 李士伦. 模糊神经网络及其在储集层油气评价中的应用. 钻采工艺. 1977.Vol.20(1):32-36
[185] 邹开其. 模糊神经网络的若干进展. 浙江万里学院学报.2003.Vol.16(4):54-57
[186] Horikaws S I, et al. On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior. Int. J. Fuzzy sets and systems, Vol. 71, No.1,1995
[187] Harris C J, Moore C G. Phase plane analysis tools for a class of fuzzy control systems. Proceedings of FUUZ-IEEE'92, 1992. 511-518
[188] Hwang G-C, Lin S-C. A stabilities approach to fuzzy control design for nonlinear systems. Fuzzy Sets and Systems, 1992. Vol. 48:279-287
[189] Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and Systems. 1998. vol.28.no.1.15-13
[190] Sugeno M, Tanaka K. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems. 1992.vol.45.no.2.135-156
[191] 尹红军. 试井分析的最优化方法及其应用. 大庆石油学院博士论文. 1999
[192] Holland. J. H. Adaptation in Neural Artificial Systwm the Univ. Of Michigon Press,Michigon. 1975, 10-56
[193] Bageley. J. D. The Behavior of Adaptive System Which Emloy Genetic and Correlation Algorithms .Dissertation Abstracts International. 1976, 28(12): 1231-1240
[194] Hollstien. R. B. Artificial genetic adaptation in computer Control systems (Doctoral dissertation,University of Michigan ). Dissertation Abstracts Internationa. 1971, 2(3).1510B
[195] De Jong K.A, An Analysis of the Behaviour of a Class of Genetic adaptive System:[ph D Dissertation ].University of Michigan. 1975, 76-93
[196] Goldberg .D. E Genetic Algorithms in Search Optimization and Machine Reading. MA: Addison Wesley. 1983
[197] Fitzpartrick,J.N.,Grefenstette,J,J. and Van Gucht,D., Image registration by genetic Algorithms search. Proceedings of IEEE Southeast Conference. 1984, 460-464
[198] Davis,L(Ed.). Handbook of Genetic Algorithms. Van Nostrand Reinhold. New York. 1991
[199] 孙艳丰等. 遗传算法在优化问题中的应用研究进展. 控制与决策. 1996, 11(4): 425-431
[200] 艾丽蓉,何华灿. 遗传算法综述. 计算机应用研究. 1997, 14(4): 3-6
[201] 陈国良. 遗传算法及其应用. 人民邮电出版社. 1996
[202] 刘勇,康立山. 非数值并行算法—遗传算法. 1997
[203] 盂庆春著. 遗传算法及其应用. 山东人学山版社. 1995
[204] 张文修,梁怡编著. 遗传算法的数学基础. 西安交通大学出版社. 2000
[205] Wright A.H. Genetic Algorithms for Real Parameter Optimization . Functions of Genetic Algorithms. San Matei, CA:Morgan Kaufmann. 1991, 205-218
[206] Qi X.,Pal-Mieri F. Theoretical Analysis of Evolutionary Algorithms With an Infinite Population Size in Continuous Space Part I: Basic properties of Selection and Mutation .IEEE Trans.on Neural network. 1994, 5(1): 102-129
[207] Goldberg D.E.,Segreat.P. Finite Markov chain Analysis of Genetic Algorithm. Proceedings of the 2nd International Convergence on Gas. Massachusette institute of Technology Cambridge ,MA. 1987,1-8
[208] Eiben A.E, et.al .Global Convergence of Genetic Algorithms: An Inifinite Markov Chain Analysis. Parallel Problem Solving From Nature .Berlin: Spring-Verlg . 1991, 4-12
[209] Rudolph G. Convergence Analysis of Genetic algorithms. IEEE Trans .On Neural network . 1994, 5(1): 96-101
[210] Grefenstette J.J. GA for The traveling Salesman Problem .Proc. of int.Con.on.GA and their Appl .Lawrence Earlbaum. 1985, 160-168
[211] Ludmila. I , F . Kuncheva . Fitness Functions in Editing k-NN Reference Set by Genetic Algorithms.Pattern Recognition. 1997, 30(6): 1042-1049
[212] 刘宝坤,石江瑞,王慧,赵丛. 基于遗传算法的神经网络自适应控制器的研究. 信息与控制. 1997, 26(4): 311-314
[213] 黄诗峰 等,地质统计学中变差函数参数估计的新方法,地质与勘探,1999,1. 41-43
[214] Steve. A ,Billings,and Guang L.zheng.Radial Basis Function Network Configuration Using Genetic A1gorlthms.Neural Network. 1995, 8(6): 877-890
[215] Nello Cristianini. John Shawe-Taylor. 支持向量机导论. 李国正,王猛等译. 北京: 电子工业出版社. 2005.1
[216] Vapnik. V. The Nature of Statistical Learning Theory.Springer Verlag,1995.
[217] Vapnik. V. Statistical Learning Theory.Wiley,1998.
[218] Duda, R.O. and P.E. Hart. Patter Classification and Scene Analysis. Wiley. 1973
[219] Cover, T.M. Geometrical and statistical properties of systems of linear nequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 1965. 14:326-334
[220] Vapnik, V. and A. Lerner. Pattern recognition using generalized portrait method. 1963. Automation and Remote Control, 24.
[221] Anlauf, J.K. and M. Biehl . The adatron: an adaptive perceptron algorithm. Europhysics Letters, 1989. 10:687-692
[222] Aronszajn, N. Theory of reproducing kernels. Transactions of the American athematical Society, 1950.68:337-404
[223] Poggio, T. On optimal nonlinear associative recall. Biological Cybernetics, 1975. 19:201 -209
[224] Aizerman, M., E. Braverman, and L. Rozonoer. Theoretical foundations of the potent -ial function method in pattern recognition learning. Automation and Remote Control .1964. 25:821-837
[225] Mangasarian, O. L. Linear and nonlinear separation of patterns by linear programming. Operations Research, 1965.13:444--452
[226] Smith, F.W. Pattern classifier design by linear programming. IEEE Transactions on Computers, 1968. C-17:367-372
[227] Mangasarian, O. L. Multi-surface method of pattern separation. IEEE Transactions onInformation Theory , 1968. IT-14:801-807
[228] Boser, B.E, I.M. Guyon and V. Vapnik. A Training Algorithm for Optimal Margin Classifiers, Fifth Annual Workshop on Computational Learning Theory, ACM. 1992
[229] Anthony, M. and P. Bartlett. Learning in Neural Networks : Theoretical Foundations. Cambridge University Press. 1999
[230] Cristianini, N. and Shawe-Taylor, J. AN INTRODUCTION TO SUPPORT VECTOR MACHINES (and other kernel-based learning methods). Cambridge University Press 2000
[231] Wakamatsu, Y, M. Mochii, K.S. Vogel, and J.A. Weston. Avian neural crest-derived neurogenic precursors undergo apoptosis on the lateral migration pathway. Development. 1998. 125, 4205-4213
[232] Smola, A.J. and B. Sch?lkopf . A Tutorial on Support Vector Regression. Produced as part of the ESPRIT Working Group in Neural and Computation Learning , NeuroCOLT2. Technical Report Series NC2-TR-1998-030, October 1998
[233] Schoelkopf, B., C.J.C. Burges, and A.J. Smola. Advances in kernel methods – support vector learning, MIT Press, Cambridge, MA. 1999
[234] Tikhonov, A. N. Solution of incorrectly formulated problems and the regularization method. Soviet Math. Dokl., 1963. 4:1035-1038
[235] Poggio, T. and F. Girosi. Regularization Algorighms for Learning That Are Equivalent to Multilayer Networks. Science. 1990. 247:pp. 978-982
[236] Smola, A.J., P. Bartlett, B. Schoelkopf, and C. Schuurmans. Advances in large margin classifiers, MIT Press, Cambridge, MA. 1999
[237] Smola, A. J. Learning with kernel. Ph.D Thesis, 1998
[238] Mangasarian O.L. and D. R. Musicant. Nonlinear Data Discrimination via Generalized Support Vector Machines ICCP99: International Conference on Complementarity Problems,Madison, Wisconsin, 1999. June 9-12, 19.
[239] Cortes C. and V. Vapnik. Support vector networks. Machine Learning, 1995. vol.20, pp. 273 –297
[240] Osuna, E, R. Freund, F. Girosi. An improved training algorithm for support vector machines, Proc. IEEE Neural Netw For sign. Proc. 1997. NNSP 97, 276-285
[241] Joachims, T, Making Large-Scale Support Vector Machines Learning Practical. In Advances in Kernel Methods, 169-184, 1998
[242] Platt, J.C. Sequential Min imal Optimisation: a fast algorithm for training support vector machines, Techn. Rep. MSR-TR-98-14, Microsoft Research. 1998
[243] Cauwenberghs, G. and T. Poggio. Incremental and decremental support vector mach -ine learning. Advances in Neural Information Processing Systems (NIPS*2000) 2001,13.
[244] Ralaivola L. Florence d'Alché-Buc: Incremental Support Vector Machine Learning: A Local Approach. ICANN 2001: 322-330
[245] Suykens, J.A.K. Nonlinear Modelling and Support Vector Machine, IEEE Instrument -ation and Measurement Technology Conference Budapest, Hungary, 2001.May 21-23
[246] Carozza, M. and S. Rampone. Towards an incremental SVM for regression. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy 2000, 6: 405-410
[247] Xiao Rong , Wang Jicheng and Zhang Fayan. An approach to incremental SVM learning algorithm. Proceedings of 12th IEEE International Conference on Tools with Artificial Intelligence, ancouver, BC, Canada, 2000: 268-273
[248] Vapnik, V. Principles of Risk Minimization for Learning Theory. 1991. NIPS 831-838.
[249] 张学工. 关于统计学习理论与支持向量机. 自动化学报.26(1). 2000
[250] Hearst M.A, B. Scholkopf and Dumais Setal. Trends and controversies-support vector machines. IEEE Intelligent Systems, 1998, 13 (4): 18-28
[251] Keren, D, M. Osadchy and C. Gotsman. Antifaces: A novel, fast method for image detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23 (7):747-761
[252] Vailaya A, H. Zhang, Zhang HongJiang et al. Automatic image orientation detection. IEEE Transactions on Image Processing, 2002, 11(7): 746 –755
[253] Guo Guo-Dong A.K.Jain, Ma Wei-Ying et al. Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks, 2002, 13 (4): 811-820
[254] Brown, M, H.G. Lewis, S.R. Gunn. Linear spectral mixture models and support vector machines for remote sensing, (submitted to)IEEE Trans. Geoscience and Remote Sensing. 2000
[255] Xin Dong and Wi Zhaohui. Speaker recognition using continuous density support vector machines. Electronics Letters, 2001, 37 (17-16): 1099-1101
[256] Bahlmann C., B. Haasdonk and H. Burkhardt. On-line handwriting recognition with support vector machines -a kernel approach. Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, 2002: 49-54
[257] Kim K.I, J. Kim and K. Jung. Recognition of facial images using support vector machine. Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001: 468-471, Singapore.
[258] Drezet P.M.L. and R.F. Harrison. Support vector machines for system identification. UKACC International Conference on Control '98, 1998, 1 (1):688-692
[259] De Kruif B.J. and T.J.A. de Vries. On using a support vector machine in learning feed-forward control. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2001, 1 (1):272-277
[260] Rychetsky M., S. Ortmann and M. Glesner. Support Vector Approaches for Engine Knock Detection. International Joint Conference on Neural Networks (IJCNN 99), 1999, Washington, USA.
[261] Gao Junfeng, Shi Wengang, Tan Jianxun et al. Support vector machines based approach for fault diagnosis of valves in reciprocating pumps. Proceedings of IEEE Conference on Electrical and Computer Engineering, 2002, 3.1622 -1627 Canadian.
[262] J.Weston and C.Watkins. Support vector machines for multi-class pattern recognition. In Proceeding of the 6th European symposium on Artificial Neural Network (ESANN),1999
[263] J.Platt,N.Cristianini,and J.Shawe-Taylor. Large margin DAGs for multiclass classification. In Neural Information Processing System (NIPS99),1999.to appear.
[264] Zhang Qinghua.. Regressor selection and wavelet network construction. INRIA Technical Report. No.709-719
[265] 张邦礼,李银国等.小波神经网络的构造及其算法的鲁棒性分析.重庆大学学报. 1995, 18(6): 88-95
[266] 李银国,张邦礼等.小波神经网络及其结构设计方法.模式识别与人工智能. 1997, 9(3): 197-205
[267] 吴耀军等. B 样条小波神经网络.模式识别与人工智能.1996,9(3):22-233
[268] 水鹏朗,保 铮,焦李成. 一种基于子波神经网络的多尺度差分方程求解新方法. 电子科学学刊.1997, 19(6): 733-737
[269] Christopher J. C.Burges. A tutorial on support vector machine for pattern recognition. Data Mining and knowledge discovery. Vol.2,no.2. 1998
[270] 李建平编. 小波分析与信号处理—理论、应用及软件实现. 重庆出版社. 1997
[271] Song Xianyu, et al . Adaptive Wavelet neural network for pattern recognition. Nanjing: Procof IEEE ICNNSP '95. 1995, 386-389
[272] 柳振宇等. 基于不变矩和子波神经网络的图象识别. 武汉: 神经网络及应用—'94 最新进展,. 1994, 552-555
[273] 李向武,韦岗. 小波神经网络的动态系统辨识方法及应用. 控制理论与应用. 1998, 15(4) : 494-500
[274] 金龙等. 基于小波神经网络的长期预报研究. 南 京: CCNS'97 (二). 1997, 617-620
[275] 李建平等. 基于小波变换和神经网络的图象压缩. 成都: 神经网络理论与应用研究 '96. 1996, 436-430
[276] LiuWei, et al. The compression of Irspectra by using wavelet neural network: Chinese Science Bulletin. 1997, 10: 822-825