知识发现技术在赤潮分析预测中的应用研究
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摘要
众所周知,21世纪是海洋世纪,海洋包括高新技术引导下的海洋经济将成为国际竞争的主要领域。在我国,赤潮灾害发生范围已遍及所有沿海省市,每年对海产养殖业、滨海旅游业等海洋经济产业造成的直接经济损失高达数以十亿元计。因此,研究赤潮分析预测关键技术和开发先进的赤潮预警应用系统对促进我国海洋经济可持续发展具有重要的现实意义和战略意义。
赤潮是一种由多种因素综合作用引发的生态异常现象,具有随机性、模糊性、突发性和非线性等特点,对其建模和预测是非常困难的。赤潮现象的这些特点使得赤潮灾害的预测预警已成为当今海洋科学技术领域最具挑战性的难题之一。传统的研究思路大多数是从赤潮现象的某一个特点出发构建算法模型,并设计开发原型系统。这些模型只是从某一个方面反映赤潮的特性,而没有全面系统地刻画赤潮的本质和各方面的特征,实际应用的效果并不是很理想。为了克服传统研究思路的不足,需要不断引入新的理论和方法,同时需要将多种不同技术方法综合集成,从整体的观点对赤潮进行系统和全面的分析研究。
本论文以山东省科学发展计划(重点项目)“海洋环境在线监测及灾害智能预警系统的研制(2004GG2205108)”为背景,在总结吸收前人研究成果的基础上,探索以知识发现的理论和方法为框架,多学科交叉综合研究,考虑将小波分析、主元分析、模糊聚类分析和神经网络等知识发现技术综合应用于赤潮数据资料的分析处理上,并在课题组众多研究成果的基础上设计实现赤潮知识发现平台,通过该平台构建多种组合模型,以期能从更多视角认识和掌握赤潮发生的内在规律,进而在赤潮预测预警研究上取得一定的成果。
全文的主要内容包括以下几个方面:
(1)首先在分析传统小波阈值去噪方法的基础上,提出一种基于平移不变的小波指数阈值去噪方法,并通过理论分析和仿真实验对其去噪性能进行了深入地分析和探讨,结果表明该方法能够有效克服传统硬阈值和软阈值去噪方法的不足,较为明显地提升了去噪性能,具有一定的通用性。进而针对赤潮分析预测中使用的数据资料可能受到大量噪声影响的问题,应用该方法对其进行去噪处理,获得了较高品质的数据资料,能够更为真实和客观地反映赤潮现象的本质特征,从而为后续的赤潮分析预测等提供了可靠和有效的数据源。
(2)其次,在分析研究常规模糊c均值聚类(Fuzzy C-meansClustering,FCM)算法不足的基础上,针对FCM算法在聚类中心的迭代计算过程中,各样本点对聚类中心的确定具有相同的权重,容易导致少数异常样本的存在也会对聚类中心的确定产生较大影响的问题,提出了一种基于相似关系的模糊加权FCM算法(Fuzzy Weighting FCM,FWFCM),根据各样本对聚类中心的不同影响程度,为每一个样本设置了一个特征权值,使得各个样本在聚类中心的迭代计算中起到不同的作用。进而针对目前大多数赤潮预测算法不能合理区划赤潮起始、发生、发展和消亡的演变过程,应用FWFCM算法,找出比较可靠的赤潮生态过程所处阶段的范围划分规则,不仅得到了较准确的和符合实际的类别划分,直观和真实的反映了赤潮生态过程的模糊性和过渡性等特征,而且在分类后仍极大地保留了样本数据集的原始信息,为进一步探求分类的内在原因和解释样本点之间彼此关联的程度提供了有效的途径。
(3)然后,针对传统的单一预测模型不能全方面刻画赤潮生态现象的本质特征,存在预测精度低和预测结果稳定性差的不足,以及神经网络预测模型对预测结果缺乏合理性解释的问题,提出了融合主元分析、模糊聚类分析、小波分析和神经网络的赤潮组合预测模型,使用主元分析方法和模糊聚类算法先将赤潮生态过程分解为若干不同本质的子过程,然后再应用小波网络对赤潮藻类密度等进行预测预报。该模型结构在一定程度上反映了赤潮生态过程的内在规律,可以对模型的输出结果给予更合理的解释。同时,在该组合模型在模型效率和预测精度方面都较单一预测模型有所提升。
(4)最后,针对传统赤潮预测预警软件中普遍存在的模型单一化、数据与算法过耦合,以及可扩展性和可重用性差等问题,在深入研究知识发现过程机理、系统结构和运行机制,以及软件架构模型的基础上,提出了一种基于组件技术的面向服务的赤潮知识发现平台的解决方案,以期为用户提供一个可以快速和灵活构建各种组合模型,并能够测试和实际运行的赤潮分析预测的研究与应用环境。
总之,本文在知识发现技术体系框架下,综合运用多种理论和方法,多视角、多层次系统全面地研究了赤潮的分析预测。大量实例分析表明,本文的研究思路具有较强的可行性和实用性,深化、丰富和发展了赤潮分析预测的理论和方法,为赤潮的预测预警研究提供了新途径。
It is well known that the 21~(st) century is called "marine century". Marine and high-tech-driven marine economy is increasingly becoming major field of international competition.Red tide has brought about many negative environmental and economic consequences around,along the coastal areas of China in recent years.The annual direct economic loss caused by red tide has amounted to much as 10 billion Yuan average. Therefore,research on key technology of analysis and forecast for red tide,and development of red tide early warning application systems has important practical and strategic significance on promoting sustainable development of marine economy in China.
Red tide is an anomalous ecological phenomenon caused by various complex factors and is characterized by random,fuzziness,abruptness and nonlinearity,so it is difficult to build up red tide prediction model. All the features of red tide make the research on the red tide prediction one of the most challenging subjects in the frontier field of marine science and technology.To overcome the shortcomings of traditional ways,new theories and methods must be continuously introduced.At the same time,different methods must be integrated to research red tide systematically and comprehensively in a macro view.
This thesis is written against the background of the project named Marine Environment Observing and Calamity Intelligent Early Warning System,which is supported by Science and Technology Development Plan (Key Project) of Shandong Province(Grant No.2004GG2205108).On the basis of previous achievements,by taking theories and methods of knowledge discovery as a framework,and cross-subject integrative research in analysis and prediction of red tide,this thesis applies knowledge discovery techniques(such as wavelet transform,principal component analysis,fuzzy clustering and neutral network) to data analysis and processing of red tide.At the same time,a red tide knowledge discovery platform is designed and implemented,thanks to numerous research achievements of our team.Hopefully,through construction of composition models based on the platform,researchers can fully and correctly understand the nature and internal mechanism of red tide from multi points of view,and then fruit certain achievements on the prediction of red tide.
The thesis consists of the major novel technical contributions as follows:
Firstly,grounded on the analysis of the traditional threshold methods,a de-noising method of wavelet exponential threshold is put forward,based on translation invariance.It proves that this method has effectively overcome the shortcomings of hard shrinkage and soft shrinkage function, and greatly improved the performance of de-noising.In response to the fact that the measurement data of red tide may be inevitably affected by noise and therein,the proposed method is applied to de-noise the data in order to achieve high-quality ones.Thus,the nature of red tide is more objectively and truly reflected through the de-noised measurement data, which provides a reliable and valid data source for the subsequent analysis and prediction of red tide.
Secondly,this thesis studies fuzzy clustering theory and algorithms.In conventional FCM algorithm,every sample has the same influence on data set classification,but it is not often correct in practical classification process.This sometimes causes the fact that the ascertainment of cluster centers is strongly influenced by a few anomalous samples in data set.In order to solve the problem,a new fuzzy weighting clustering algorithm based on the fuzzy C-means algorithm and similar relation is proposed. When the fuzzy weighting coefficient is applied to the cluster centers and Euclidean distance,each sample has various influences on data set classification in FCM algorithm.The convenient prediction algorithm for red tide is not able to make reasonable distinction between the red tide evolutionary phases including its origin,occurrence,maintenance and dying.Therefore,the FWFCM algorithm is applied to find out the reliable division rules for different phases of evolutionary process of red tide. More accurate and authentic division is achieved,which reflects the fuzziness and transitivity of red tide intuitively and truly,and retains original information of the data sets.Thus,the proposed strategy provides a valid way to explore inherent reason of division and explain correlation degree between samples.
Thirdly,a red tide combined prediction model is proposed,which is the integration of principle component analysis,fuzzy clustering algorithm, wavelet transform and neural network.The model reflects the inner mechanism of red tide to a certain extent,and explains the predictive result in a reasonable and clear manner.What's more,this combined model efficiency increases the model efficiency and predictive accuracy.
Fourthly,the inner mechanism of knowledge discovery system and software architecture model is studied.To solve the common problems (such as simplified model,coupling of data and algorithm,lack of expandability and reusability,etc.) of traditional red tide prediction software,a solution of red tide knowledge discovery platform is put forward,based on component technology and service oriented architecture.Through the convenient environment provided by the proposed solution,users could build various combined models,test models,and execute in actual business.
In short,the thesis takes theories and methods of knowledge discovery as a framework,and integrates cross-subject research in analysis and prediction of red tide.Lots of case studies show that the research has enriched and deeply developed the theory and method of the analysis and prediction of red tide with its practicability and effectiveness.It provides a new approach to the research on red tide prediction.
赤潮是一种由多种因素综合作用引发的生态异常现象,具有随机性、模糊性、突发性和非线性等特点,对其建模和预测是非常困难的。赤潮现象的这些特点使得赤潮灾害的预测预警已成为当今海洋科学技术领域最具挑战性的难题之一。传统的研究思路大多数是从赤潮现象的某一个特点出发构建算法模型,并设计开发原型系统。这些模型只是从某一个方面反映赤潮的特性,而没有全面系统地刻画赤潮的本质和各方面的特征,实际应用的效果并不是很理想。为了克服传统研究思路的不足,需要不断引入新的理论和方法,同时需要将多种不同技术方法综合集成,从整体的观点对赤潮进行系统和全面的分析研究。
本论文以山东省科学发展计划(重点项目)“海洋环境在线监测及灾害智能预警系统的研制(2004GG2205108)”为背景,在总结吸收前人研究成果的基础上,探索以知识发现的理论和方法为框架,多学科交叉综合研究,考虑将小波分析、主元分析、模糊聚类分析和神经网络等知识发现技术综合应用于赤潮数据资料的分析处理上,并在课题组众多研究成果的基础上设计实现赤潮知识发现平台,通过该平台构建多种组合模型,以期能从更多视角认识和掌握赤潮发生的内在规律,进而在赤潮预测预警研究上取得一定的成果。
全文的主要内容包括以下几个方面:
(1)首先在分析传统小波阈值去噪方法的基础上,提出一种基于平移不变的小波指数阈值去噪方法,并通过理论分析和仿真实验对其去噪性能进行了深入地分析和探讨,结果表明该方法能够有效克服传统硬阈值和软阈值去噪方法的不足,较为明显地提升了去噪性能,具有一定的通用性。进而针对赤潮分析预测中使用的数据资料可能受到大量噪声影响的问题,应用该方法对其进行去噪处理,获得了较高品质的数据资料,能够更为真实和客观地反映赤潮现象的本质特征,从而为后续的赤潮分析预测等提供了可靠和有效的数据源。
(2)其次,在分析研究常规模糊c均值聚类(Fuzzy C-meansClustering,FCM)算法不足的基础上,针对FCM算法在聚类中心的迭代计算过程中,各样本点对聚类中心的确定具有相同的权重,容易导致少数异常样本的存在也会对聚类中心的确定产生较大影响的问题,提出了一种基于相似关系的模糊加权FCM算法(Fuzzy Weighting FCM,FWFCM),根据各样本对聚类中心的不同影响程度,为每一个样本设置了一个特征权值,使得各个样本在聚类中心的迭代计算中起到不同的作用。进而针对目前大多数赤潮预测算法不能合理区划赤潮起始、发生、发展和消亡的演变过程,应用FWFCM算法,找出比较可靠的赤潮生态过程所处阶段的范围划分规则,不仅得到了较准确的和符合实际的类别划分,直观和真实的反映了赤潮生态过程的模糊性和过渡性等特征,而且在分类后仍极大地保留了样本数据集的原始信息,为进一步探求分类的内在原因和解释样本点之间彼此关联的程度提供了有效的途径。
(3)然后,针对传统的单一预测模型不能全方面刻画赤潮生态现象的本质特征,存在预测精度低和预测结果稳定性差的不足,以及神经网络预测模型对预测结果缺乏合理性解释的问题,提出了融合主元分析、模糊聚类分析、小波分析和神经网络的赤潮组合预测模型,使用主元分析方法和模糊聚类算法先将赤潮生态过程分解为若干不同本质的子过程,然后再应用小波网络对赤潮藻类密度等进行预测预报。该模型结构在一定程度上反映了赤潮生态过程的内在规律,可以对模型的输出结果给予更合理的解释。同时,在该组合模型在模型效率和预测精度方面都较单一预测模型有所提升。
(4)最后,针对传统赤潮预测预警软件中普遍存在的模型单一化、数据与算法过耦合,以及可扩展性和可重用性差等问题,在深入研究知识发现过程机理、系统结构和运行机制,以及软件架构模型的基础上,提出了一种基于组件技术的面向服务的赤潮知识发现平台的解决方案,以期为用户提供一个可以快速和灵活构建各种组合模型,并能够测试和实际运行的赤潮分析预测的研究与应用环境。
总之,本文在知识发现技术体系框架下,综合运用多种理论和方法,多视角、多层次系统全面地研究了赤潮的分析预测。大量实例分析表明,本文的研究思路具有较强的可行性和实用性,深化、丰富和发展了赤潮分析预测的理论和方法,为赤潮的预测预警研究提供了新途径。
It is well known that the 21~(st) century is called "marine century". Marine and high-tech-driven marine economy is increasingly becoming major field of international competition.Red tide has brought about many negative environmental and economic consequences around,along the coastal areas of China in recent years.The annual direct economic loss caused by red tide has amounted to much as 10 billion Yuan average. Therefore,research on key technology of analysis and forecast for red tide,and development of red tide early warning application systems has important practical and strategic significance on promoting sustainable development of marine economy in China.
Red tide is an anomalous ecological phenomenon caused by various complex factors and is characterized by random,fuzziness,abruptness and nonlinearity,so it is difficult to build up red tide prediction model. All the features of red tide make the research on the red tide prediction one of the most challenging subjects in the frontier field of marine science and technology.To overcome the shortcomings of traditional ways,new theories and methods must be continuously introduced.At the same time,different methods must be integrated to research red tide systematically and comprehensively in a macro view.
This thesis is written against the background of the project named Marine Environment Observing and Calamity Intelligent Early Warning System,which is supported by Science and Technology Development Plan (Key Project) of Shandong Province(Grant No.2004GG2205108).On the basis of previous achievements,by taking theories and methods of knowledge discovery as a framework,and cross-subject integrative research in analysis and prediction of red tide,this thesis applies knowledge discovery techniques(such as wavelet transform,principal component analysis,fuzzy clustering and neutral network) to data analysis and processing of red tide.At the same time,a red tide knowledge discovery platform is designed and implemented,thanks to numerous research achievements of our team.Hopefully,through construction of composition models based on the platform,researchers can fully and correctly understand the nature and internal mechanism of red tide from multi points of view,and then fruit certain achievements on the prediction of red tide.
The thesis consists of the major novel technical contributions as follows:
Firstly,grounded on the analysis of the traditional threshold methods,a de-noising method of wavelet exponential threshold is put forward,based on translation invariance.It proves that this method has effectively overcome the shortcomings of hard shrinkage and soft shrinkage function, and greatly improved the performance of de-noising.In response to the fact that the measurement data of red tide may be inevitably affected by noise and therein,the proposed method is applied to de-noise the data in order to achieve high-quality ones.Thus,the nature of red tide is more objectively and truly reflected through the de-noised measurement data, which provides a reliable and valid data source for the subsequent analysis and prediction of red tide.
Secondly,this thesis studies fuzzy clustering theory and algorithms.In conventional FCM algorithm,every sample has the same influence on data set classification,but it is not often correct in practical classification process.This sometimes causes the fact that the ascertainment of cluster centers is strongly influenced by a few anomalous samples in data set.In order to solve the problem,a new fuzzy weighting clustering algorithm based on the fuzzy C-means algorithm and similar relation is proposed. When the fuzzy weighting coefficient is applied to the cluster centers and Euclidean distance,each sample has various influences on data set classification in FCM algorithm.The convenient prediction algorithm for red tide is not able to make reasonable distinction between the red tide evolutionary phases including its origin,occurrence,maintenance and dying.Therefore,the FWFCM algorithm is applied to find out the reliable division rules for different phases of evolutionary process of red tide. More accurate and authentic division is achieved,which reflects the fuzziness and transitivity of red tide intuitively and truly,and retains original information of the data sets.Thus,the proposed strategy provides a valid way to explore inherent reason of division and explain correlation degree between samples.
Thirdly,a red tide combined prediction model is proposed,which is the integration of principle component analysis,fuzzy clustering algorithm, wavelet transform and neural network.The model reflects the inner mechanism of red tide to a certain extent,and explains the predictive result in a reasonable and clear manner.What's more,this combined model efficiency increases the model efficiency and predictive accuracy.
Fourthly,the inner mechanism of knowledge discovery system and software architecture model is studied.To solve the common problems (such as simplified model,coupling of data and algorithm,lack of expandability and reusability,etc.) of traditional red tide prediction software,a solution of red tide knowledge discovery platform is put forward,based on component technology and service oriented architecture.Through the convenient environment provided by the proposed solution,users could build various combined models,test models,and execute in actual business.
In short,the thesis takes theories and methods of knowledge discovery as a framework,and integrates cross-subject research in analysis and prediction of red tide.Lots of case studies show that the research has enriched and deeply developed the theory and method of the analysis and prediction of red tide with its practicability and effectiveness.It provides a new approach to the research on red tide prediction.
引文
[1]ZINGONE Adriana,ENEVOLDSEN Henrik Oksfeldt.The diversity of harmful algal blooms:a challenge for science and management[J].Ocean & coastal management,2000,43:725-748.
[2]杨华庭.近十年来的海洋灾害与减灾[J].海洋预报,2002,19(1):2-8.
[3]国家海洋局.中国海洋灾害公报[EB/OL].[2009].http://www.soa.gov.cn/hyjww/hygb/zghyzhgb/A020708index_1.htm.
[4]周名江,朱明远.“我国近海有害赤潮发生的生态学、海洋学机制及预测防治”研究进展[J].地球科学进展,2006,21(7):673-681.
[5]Anderson D M,Andersen P,Bricelj V M,et al.Red Tide-HAB Monitoring and Management in Hong Kong,Final Report[Z].Hong Kong:Prepared for the Agriculture and Fisheries Department,1999.
[6]周名江,朱明远,张经.中国赤潮的发生趋势和研究进展[J].生命科学,2001,13(2):54-60.
[7]苏纪兰.中国的赤潮研究[J].中国科学院院刊,科技与社会,2001,(5):339-342.
[8]冯士筰,李凤岐,李少菁.海洋科学导论[M].北京:高等教育出版社,2000.
[9]张水浸等.赤潮及防治对策[M].北京:海洋出版社,1994.
[10]LoCicero V R.Proceedings of the First International Conference on Toxic D inoflagellate B looms[Z].Boston,Massachusetts:Massachusetts Science and Technology Foundation,1975.
[11]GEOHAB,2001.Global Ecology and Oceanography of Harmful Algal Blooms,Science Plan.P.Glibert and G.Pitcher(eds).SCOR and IOC,Baltimore and Paris.87 pp.
[12]许卫忆,朱德弟.赤潮生消过程的数值模拟与对赤潮研究的几点认识:赤潮监测与预报研究论文选编[C].杭州:国家海洋局第二海洋研究所,2000.
[13]王洪礼,冯剑丰.赤潮生态动力学与预测[M].天津:天津大学出 版社,2006.
[14]王正方,张庆,吕海燕等.长江口溶解氧赤潮预报简易模式[J].海洋学报,2000,22(4):125-129.
[15]矫晓阳.透明度作为赤潮预警监测参数的初步研究[J].海洋环境科学,2001,20(1):27-31.
[16]林祖亨,梁舜华.探讨影响赤潮的物理因子及其预报[J].海洋环境科学,2002,21(2):1-5.
[17]张正斌,刘春颖,等.利用化学因子预测赤潮的可行性探讨[J].青岛海洋大学学报(自然科学版),2003,33(2):257-263.
[18]矫晓阳.叶绿素a预报原理探索[J].海洋预报,2004,21(2):56-63.
[19]ZHANG Junfeng,et al.Forecast of Red Tide in the South China Sea by Using the Variation Trend of Hydrological and Meteorological Factors[J].Marine Science Bulletin,2006,8(2):60-74.
[20]林祖享,梁舜华.探讨运用多元回归分析预报赤潮[J].海洋环境科学,2002,21(3):1-4.
[21]谢中华,王洪礼,史道济,孙景.运用混合回归模型预报赤潮[J].海洋技术,2004,23(1):27-30.
[22]Qiuwen Chen and Arthur E.Mynett.Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression[J].Ecological Modelling,2004,176(3-4):277-290.
[23]W Hongli,F Jianfeng,LI Shengpeng,S Fei.Statistical Analysis and Prediction of the Concentration of Harmful Algae in Bohai Bay[J].TRANSACTIONS OF TIANJIN UNIVERSITY,2005,11(4):308-312.
[24]王洪礼,向建平,葛根.多元自适应样条回归预报浮游植物总量分析[J].海洋技术,2006,25(3):7-9.
[25]王寿松,夏综万.大鹏湾夜光赤潮的营养动力学模型[J].热带海洋,1997,16(1):1-6.
[26]许卫忆,朱德弟等.实际海域的赤潮生消过程数值模拟[J].海洋与湖沼,2001:32(6):598-604.
[27]陈扬,赖锡军.赤潮生态动力学模型初探[J].海洋湖沼通报,2003(4):84-89.
[28]王洪礼,葛根,李怀宇.赤潮藻类生态营养模型非线性动力研究[J].海洋技术,2005:24(4):41-43.
[29]G.A.Gibson,D.L.Musgrave,and S.Hinckley.Non-linear dynamics of a pelagic ecosystem model with multiple predator and prey types[J].Journal of Plankton Research,2005,27(5):427-447.
[30]S GAKKHAR,K NEGI.A mathematical model for viral infection in toxin producing phytoplankton and zooplankton system[J].Applied Mathematics and Computation,2006,179(1):301-313.
[31]Ken T.M.Wonga,Joseph H.W.Leea,I.J.Hodgkissb.A simple model for forecast of coastal algal blooms[J].Estuarine,Coastal and Shelf Science,2007,74(1-2):175-196.
[32]Friedrich Recknagel.ANNA-Artificial Neural Network model for predicting species abundance and succession of blue-green algae[J].Hydrobiologia,1997,349(1-3):47-57.
[33]F Recknagel,M French,P Harkonen,KI Yabunaka.Artificial neural network approach for modelling and prediction of algal blooms[J].Ecological Modelling,1997,96(1-3):11-28.
[34]Yabunaka K.-i.,Hosomi M.,Murakami A.Novel application of a back-propagation artificial neural network model formulated to predict algal bloom[J].Water Science and Technology,1997,36(5):89-97(9).
[35]吴京洪,李锦蓉.人工神经网络预报浮游植物生长趋势的研究[J].中山大学学报(自然科学版),2000,39(6):54-58.
[36]Joseph H.W.Lee,Yan Huang,Mike Dickman and A.W.Jayawardena.Neural network modelling of coastal algal blooms[J].Ecological Modelling,2003,159(2-3):179-201.
[37]杨建强,高振会,孙培艳,石强,李钦亮.自组织神经网络在有害赤潮预警研究中的应用[J].海洋环境科学,2003,22(1):6-9.
[38]ZHANG Rong,YAN Hong,DU Liping.Research on Prediction of Red Tide Based on Fuzzy Neural Network[J].Marine Science Bulletin,2006,8(1):83-91.
[39]高强,宋神,杜忠晓.基于T-S模糊神经网络的信息融合在赤潮预测 预警中的应用[J].海洋技术,2006,25(2):103-106.
[40]H Wilson,F Recknagel.Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes[J].Ecological Modelling,2001,146(1-3):69-84.
[41]沈菲,曲阳,李会民,冯剑丰,王洪礼.浮游植物密度的人工神经网络预测研究[J].海洋技术,2005,24(1):44-47.
[42]Nitin Muttil,Kwok-Wing Chau.Neural network and genetic programming for modelling coastal algal blooms[J].International Journal of Environment and Pollution,2006,28(3/4):223-238.
[43]L.Velo-Suarez and J.C.Gutierrez-Estrada.Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva(Western Andalucia,Spain)[J].Harmful Algae,2007,6(3):361-371.
[44]Shen,Q.and Chouchoulas,A..FuREAP:a fuzzy-rough estimator of algae populations.Artificial Intelligence in Engineering,2001,15(1):13-24.
[45]陆文聪,杨柳等.支持向量机算法用于夜光藻密度建模[J].计算机与应用化学,2002,19(6):706-708.
[46]Qiuwen Chen,Arthur E.Mynett.Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake[J].Ecological Modelling,2003,162(1-2):55-67.
[47]S.Marsili-libelli,G.Pacini,C.Barresi.Fuzzy prediction of the algal blooms in the Orbetello lagoon.Environmental Modelling[J].Software,2004,19:799-808
[48]Fdez-Riverola,M.Corchado,J.M.Forecasting red tides using an hybrid neuro-symbolic system[J].AI COMMUNICATIONS,2003,16(4):221-234.
[49]Fdez-Riverola,M.Corchado.CBR based system for forecasting red tides[J].Knowledge-Based Systems,2003,16(5-6):321-328.
[50]Fdez-Riverola,M.Corchado.FSfRT:Forecasting System for Red Tides[J].Applied Intelligence,2004,21(3):251-264.
[51]Chen,Qiuwen;Mynett,Arthur.Forecasting Phaeocystis globosa blooms in the Dutch coast by an integrated numerical and decision tree model[J].Aquatic Ecosystem Health & Management,2006,9(3):357-364(8).
[52]冯剑锋,王洪礼,李胜朋.基于支持向量机的浮游植物密度预测研究[J].海洋环境科学,2007,26(5):438-441.
[53]K.W.Chau.Development of an integrated knowledge-based system on flow and water quality in Hong Kong coastal waters quality in Hong Kong coastal waters[J].International Journal of Environment and Pollution,2006,28(3-4):297-309.
[54]Kwokwing Chau.A review on the integration of artificial intelligence into coastal modeling[J].Journal of Environmental Management,2006,80(1):47-57.
[55]R Wang,V Storey,C Firth.A Framework for Analysis of Data Quality Research[J].IEEE Trans Knowledge and Data Engineering,1995,(7):623-640.
[56]Jiawei Han,Micheline Kamber.Data Mining:Concepts and Techniques[M].USA:Morgan Kaufmann Publishers,2001.
[57]Daubechies I.Where do wavelets come from?—A personal point of view[J].Proc.IEEE,1996,84(5):510-513.
[58]潘泉,张磊,孟晋丽,张洪才.小波滤波方法及应用[M].北京:清华大学出版社,2005.
[59]Michel Misit,Yves Misiti,et al.Wavelets and their applications [M].ISTE Publishing Company,2007.
[60]Tao Li,Qi Li,Shenghuo Zhu,Mitsunori Ogihara.A survey on wavelet applications in data mining[J].ACM SIGKDD Explorations Newsletter,2002,4(2):49-68.
[61]蒋鹏.小波理论在信号去噪和数据压缩中的应用研究[D].浙江大学,博士学位论文,2004.
[62]胡广书.数字信号处理-理论、算法与实现(第二版)[M].北京: 清华大学出版社,2003.
[63]Mallat S.,Hwang W.L..Singularity detection and processing with wavelets[J].IEEE Trans.Inform.Theory,1992,38(2):617-643.
[64]Xu Yansun,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Trans.Image Processing,1994,3(6):747-758.
[65]Donoho D L.De-noising by soft-thresholding[J].IEEE Trans.Inform.Theory,1995,41(3):613-627.
[66]Donoho D L,Johnstone I M.,"Ideal spatial adaptation by wavelet shrinkage"[J].Biometrika,1994,81(3):425-455.
[67]Coifman R R,Donoho D L.Translation-invariant de-noising[J].Wavelets in Statistics of Lecture Notes in statistics.New York:Springer-Verlag,1994,103:125-150.
[68]Shensa,M.J.The discrete wavelet transform:wedding the a trous and Mallat algorithms[J].IEEE Trans.Signal Processing,1992,40(10):2462-2482.
[69]Lang M,et al.Noise reduction using an undecimated discrete wavelet transform[J].IEEE Signal Processing Letters,1996,3(1):10-12.
[70]Nason G P,Silverman B W.The stationary wavelet transform and some statistical applications[J].Lecture Notes in Statistics,1995,103:281-299.
[71]Fowler,J.E.The Redundant Discrete Wavelet Transform and Additive Noise[J].IEEE Signal Processing Letters,2005,12(9):629-632.
[72]Bruce A G.,Gao H Y.,"Understanding waveshrink:variance and bias estimation"[J].Biometrika,1996,83(4):727-745.
[73]安达六郎.赤潮生物と赤潮实态[J].水产土木,1973,9(1):31-36.
[74]夏综万,王寿松.大鹏湾的赤潮生态仿真模型[J].海洋与湖沼,1997,28(5):468-474.
[75]梁保松,曹殿立.模糊数学及其应用[M].北京:科学出版社,2007.
[76]Han,J.,Kamer,M.Data mining:Concepts and Techniques[M]. New York:Morgan-Kaufman,2007.
[77]吴今培,孙德山.现代数据分析[M].北京:机械工业出版社,2006.
[78]高新波.模糊聚类分析及其应用[M].西安:电子科技大学出版社,2004.
[79]Dave.R.N,Sen.S.Robust.Fuzzy Clustering of Relational Data[J].Fuzzy Systems,2002,10(6):713-727.
[80]Enrique H.Ruspini.A New Approach to Clustering[J].Information and Control,1969,15(1):22-32.
[81]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6):858-868.
[82]Rui Xu,Donald Wunsch Ⅱ.Survey of clustering algorithms[J].IEEE Trans.Neural Networks,2005,16(3):645-678.
[83]Dunn.J.C.,"A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters"[J],J.Cybernet.,1974,3(3):32-57.
[84]Dunn J.C.,"Well separated clusters and optimal fuzzy partitions".J.Cybern,1974,4:95-104.
[85]Bezedek JC.A convergence theorem for the fuzzy ISODATA clustering algorithms[J].IEEE Trans.on PAMI,1980,2(1):1-7.
[86]Bezdek,J.C.,Ehrlich,R.and Full,W..FCM:the fuzzy c-means clustering algorithm[J].Computer & Geosciences,1984,10:191-203.
[87]Jian Yu,Houkuan Huang.A New Weighting Fuzzy c-means Algorith[C].IEEE International Conference on Fuzzy System.2003:896-901.
[88]Jacek M.Leski.Generalized Weighted Conditional Fuzzy Clustering[J].IEEE TRANSACTIONS ON FUZZY SYSTEMS.2003,11(6):709-715.
[89]李洁,高新波,焦李成.基于特征加权的模糊聚类新算法[J].电子学报.2006,34(1):89-92.
[90]贾智平.嵌入式分布系统通信实时性问题的研究[D].山东大学,博士学位论文,2007.
[91] R.A.Fisher.. UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1988.
[92] Robert Hecht-Nielsen. Applications of counterpropagation networks[J]. Neural Networks,1988, 1(2): 131-139 .
[93] MCCLLOCH W S, PITTS W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 10(5): 115-133.
[94] HEBB D O. The Organization of Behavior[M]. New York: Wiley, 1949.
[95] ROSENBLATTT F. The perception: A probabilistic model for information storage and organization in the brain[J] . Psychological Review,1958,65:386-408.
[96] Marvin Minsky, Seymour Papert. Perceptrons, An Introduction to Computational Geometry[M]. MIT Press, Cambridge, Mass., 1969.
[97] MCCLELLAND J L, RUMELHARD D E. Exploration in parallel distributed processing, A Handbook of Models, Programs, and Exercises[M]. Cambridge: MIT Press. 1986.
[98] Zhang Q, A. Benveniste. Wavelet Network[J]. IEEE Trans Neural Networks. 1992, 3(6): 889-898.
[99] BAHAVIK R B, STEPHANOPOULOS G. Wave-net: a muti-resolution, hierarchical neural network with localized learning[J] . AICHE. Journal, 1993, 9(1) : 57-82.
[100] Kreinovich, V., Sirisaengtaksin, O., Cabrera, S.. Wavelet Neural Networks are Asymptotically Optimal Approximators for Functions of One Variable[C]. Proc. of 1994 IEEE International conference on Neural networks, 1994,1: 299-304.
[101] Zhang Jun. Walter Gilber G, Wavelet Neural Network for Function Learning[J]. IEEE Trans on Signal Processing, 1995, 43(6): 1485-1497.
[102] Yamakawa T., Uchino E., Samatu T. The wavelet network using convex wavelets and its application to modeling dynamical systems[J].The Trans.on the IEICE.J79-A.12,1996,P.2046-2053.
[103]Zhang Q.Using wavelet network in nonparametric estimation[J].IEEE Transactions on Neural Networks.,1997,8(2):227-236
[104]JIAO L C,PAN J,FANG Y W.,Multiwavelet neural network and its approximation properties[J].IEEE Transactions on Neural Networks.,2001,12(5):1060-1066.
[105]张茁生,刘贵忠,刘峰.一种自适应小波网络的构造及其学习算法[J].中国科学(E辑),2001,31(2):172-181.
[106]丁勇,刘守生,胡寿松.一种广义小波神经网络的结构及其优化方法[J].控制理论与应用,2003,20(1):125-128.
[107]岑翼刚,秦元庆,孙德宝,李宁.粒子群算法在小波神经网络中的应用[J].系统仿真学报,2004,(12):2783-2785(2788)
[108]StephenA.Billings,Hua-LiangWei.,A New Class Of Wavelet Networks For Nonlinear System Identification[J].IEEE Transactions on Neural Networks.,2005,16(4):862-874.
[109]Hojjat Adeli,F.ASCE and Xiaomo Jiang.Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification[J].Journal of Structural Engineering,2006,132(1):102-111.
[110]李俊民,李靖,薄立军.一类非线性动态系统的自适应模糊小波神经网络控制[J].工程数学学报,2006,23(1):37-44.
[111]Ping Guo,Hongzhai Li.Blind Image Restoration by Combining Wavelet Transform and RBF Neural Network[J].International Journal of Wavelets,Multiresolution and Information Processing,2007,5(1):15-26.
[112]Victor M.Becerra,Roberto K.Galvao,Magda Abou-Seada,Neural and Wavelet Network Models for Financial Distress Classification[J],Data Mining and Knowledge Discovery,2005,11(1):35-55.
[113]K.Vinay Kumar,V.Ravi,Mahil Carr,N.Raj Kiran,Software development cost estimation using wavelet neural networks[J].Journal of Systems and Software,2008,81(11):1853-1867.
[114]Serdar HEKIMHAN,Serdar MENEKAY,N.Serap S_ENGOR Prior Knowledge Input Method in Device Modeling[J].Turk J Elec Engin,2005.13(1):51-60.
[115]侯霞.小波神经网络若干关键问题研究[D].南京航天航空大学,博士学位论文,2006.
[116]Lindsay I Smith.A tutorial on Principal Components Analysis.http://csnet.otago.ac.nz/cosc453/student_tutorials/,2002.
[117]Shlens J.,A Tutorial on Pricipal Component analysis.http://www.snl.salk.edu/～shlens/,2005.
[118]张晓华,吴贵强.基于主元分析的传感器故障诊断[J].武汉理工大学学报(信息与管理工程版),2002,24(2):57-59.
[119]赵成燕,赵刚,刘爱伦.基于改进PCA的故障检测与诊断方法[J].石油化工自动化,2006,1:41-44.
[120]Aggarwal C,Yu P.Finding Generalized Projected Clusters in Dimensional Spaces[C]//Proc.of ACM SIGMOD Conference on Management Data.Dallas,Texas,U.S.A:[s.n.],2000:78-52.
[121]Tsai Chengfa,Tsai Chunwei,Chen Chiping.A Novel Multiple Searching Genetic Algorithm for Multimedia Multicast Routing[C].Proc.of IEEE Congress on Evolutionary Computation.Piscataway,NJ:[s.n.],2002:7065-7068.
[122]Davidson I,Satyanarayana A.Speeding Up K-means Clustering by Bootstrap Averaging[C].Proc.of IEEE Data Mining Workshop on Clustering Large Data Sets.Brighton,UK:[s.n.],2004:98-102.
[123]LukaszA.Kurgan,Petr Musilek.Asurvey of Knowledge Discovery and Data Mining process models[J].The Knowledge Engineering Review,2006,21(1):1-24.
[124]韩家炜.数据挖掘概念与技术[M].北京:机械工业出版社,2001.
[125]Margaret H.Dunham.数据挖掘教程[M].郭崇慧,田凤占,靳晓明.北京:清华大学出版社,2005.
[126]绍峰晶,于忠清.数据挖掘—原理与算法[M].北京:中国水利水电出版社,2003.
[127]毛国君,段立娟,王实,石云.数据挖掘原理与算法[M].北京:清华大学出版社,2005.
[128]Michael Goebel,Le Gruenwald.A survey of data mining and knowledge discovery software tools[J].SIGKDD Explorations,1999,1(1):20-33.
[129]恽爽,胡南军,董浚等.数据挖掘软件研究现状[J].计算机工程与应用,2003,08:189-192.
[130]李伟.软件架构的艺术[M].北京:电子工业出版社,2008.
[131]Michael S.Mimoso.A defining moment for SOA[EB/OL].[2004].http://searchsoa.techtarget.com/news/article/0,289142,sid26_gci1017004,00.html.
[132]Ali Arsanjani.Service-oriented modeling and architecture[EB/OL].[2004].http://www.ibm.com/developerworks/webservices/library/ws-soa-design1/?S_TACT=105AGX52&S_CMP=cn-a-ws.
[133]David J.N.Artus.SOA实现:服务设计原则[EB/OL].[2006].http://www.ibm.com/developerworks/cn/webservices/ws-soa-design/.
[134]Oasis.Reference Model for Service Oriented Architecture 1.0[EB/OL].[2006].http://www.oasis-open.org/committees/soa-rm/.
[135]CHEN Ying,COHEN B..Data mining and service rating in serviceoriented architectures to improve information sharing[C].Proc of IEEE,Aerospace Conference.2005:1-11.
[136]杨立,左春,王裕国.面向服务的知识发现体系结构研究与实现[J].计算机学报,2005,28(4):445-457.
[137]贾西平,彭宏,郑启伦.面向服务的数据挖掘平台体系结构研究[J].计算机应用研究,2008,25(2):440-442.
[138]任磊,王威信等.一种模型驱动的交互式信息可视化开发方法[J].软件学报,2008,19(8):1947-1964.
[139]Baron,M.et Girard,P.SUIDT.A task model based GUI-Builder[C].In Proceedings of TAMODIA:Task MOdels and DIAgrams for user interface design(July 18-19,2002,Romania,Bucharest),Inforec Printing House,2002,pp.64-71.
[140]余世银,乐嘉锦.可视化数据挖掘模型研究[J].计算机科学,2000,10(增刊):110-113.
[141]姬鹏.有害藻华监测与预报系统研究[D].西安科技大学,硕士学位论文,2006.
[142]宁勇.数据挖掘在海洋环境在线监测及赤潮灾害智能预警系统中的应用[D].山东大学,硕士学位论文,2008.
[2]杨华庭.近十年来的海洋灾害与减灾[J].海洋预报,2002,19(1):2-8.
[3]国家海洋局.中国海洋灾害公报[EB/OL].[2009].http://www.soa.gov.cn/hyjww/hygb/zghyzhgb/A020708index_1.htm.
[4]周名江,朱明远.“我国近海有害赤潮发生的生态学、海洋学机制及预测防治”研究进展[J].地球科学进展,2006,21(7):673-681.
[5]Anderson D M,Andersen P,Bricelj V M,et al.Red Tide-HAB Monitoring and Management in Hong Kong,Final Report[Z].Hong Kong:Prepared for the Agriculture and Fisheries Department,1999.
[6]周名江,朱明远,张经.中国赤潮的发生趋势和研究进展[J].生命科学,2001,13(2):54-60.
[7]苏纪兰.中国的赤潮研究[J].中国科学院院刊,科技与社会,2001,(5):339-342.
[8]冯士筰,李凤岐,李少菁.海洋科学导论[M].北京:高等教育出版社,2000.
[9]张水浸等.赤潮及防治对策[M].北京:海洋出版社,1994.
[10]LoCicero V R.Proceedings of the First International Conference on Toxic D inoflagellate B looms[Z].Boston,Massachusetts:Massachusetts Science and Technology Foundation,1975.
[11]GEOHAB,2001.Global Ecology and Oceanography of Harmful Algal Blooms,Science Plan.P.Glibert and G.Pitcher(eds).SCOR and IOC,Baltimore and Paris.87 pp.
[12]许卫忆,朱德弟.赤潮生消过程的数值模拟与对赤潮研究的几点认识:赤潮监测与预报研究论文选编[C].杭州:国家海洋局第二海洋研究所,2000.
[13]王洪礼,冯剑丰.赤潮生态动力学与预测[M].天津:天津大学出 版社,2006.
[14]王正方,张庆,吕海燕等.长江口溶解氧赤潮预报简易模式[J].海洋学报,2000,22(4):125-129.
[15]矫晓阳.透明度作为赤潮预警监测参数的初步研究[J].海洋环境科学,2001,20(1):27-31.
[16]林祖亨,梁舜华.探讨影响赤潮的物理因子及其预报[J].海洋环境科学,2002,21(2):1-5.
[17]张正斌,刘春颖,等.利用化学因子预测赤潮的可行性探讨[J].青岛海洋大学学报(自然科学版),2003,33(2):257-263.
[18]矫晓阳.叶绿素a预报原理探索[J].海洋预报,2004,21(2):56-63.
[19]ZHANG Junfeng,et al.Forecast of Red Tide in the South China Sea by Using the Variation Trend of Hydrological and Meteorological Factors[J].Marine Science Bulletin,2006,8(2):60-74.
[20]林祖享,梁舜华.探讨运用多元回归分析预报赤潮[J].海洋环境科学,2002,21(3):1-4.
[21]谢中华,王洪礼,史道济,孙景.运用混合回归模型预报赤潮[J].海洋技术,2004,23(1):27-30.
[22]Qiuwen Chen and Arthur E.Mynett.Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression[J].Ecological Modelling,2004,176(3-4):277-290.
[23]W Hongli,F Jianfeng,LI Shengpeng,S Fei.Statistical Analysis and Prediction of the Concentration of Harmful Algae in Bohai Bay[J].TRANSACTIONS OF TIANJIN UNIVERSITY,2005,11(4):308-312.
[24]王洪礼,向建平,葛根.多元自适应样条回归预报浮游植物总量分析[J].海洋技术,2006,25(3):7-9.
[25]王寿松,夏综万.大鹏湾夜光赤潮的营养动力学模型[J].热带海洋,1997,16(1):1-6.
[26]许卫忆,朱德弟等.实际海域的赤潮生消过程数值模拟[J].海洋与湖沼,2001:32(6):598-604.
[27]陈扬,赖锡军.赤潮生态动力学模型初探[J].海洋湖沼通报,2003(4):84-89.
[28]王洪礼,葛根,李怀宇.赤潮藻类生态营养模型非线性动力研究[J].海洋技术,2005:24(4):41-43.
[29]G.A.Gibson,D.L.Musgrave,and S.Hinckley.Non-linear dynamics of a pelagic ecosystem model with multiple predator and prey types[J].Journal of Plankton Research,2005,27(5):427-447.
[30]S GAKKHAR,K NEGI.A mathematical model for viral infection in toxin producing phytoplankton and zooplankton system[J].Applied Mathematics and Computation,2006,179(1):301-313.
[31]Ken T.M.Wonga,Joseph H.W.Leea,I.J.Hodgkissb.A simple model for forecast of coastal algal blooms[J].Estuarine,Coastal and Shelf Science,2007,74(1-2):175-196.
[32]Friedrich Recknagel.ANNA-Artificial Neural Network model for predicting species abundance and succession of blue-green algae[J].Hydrobiologia,1997,349(1-3):47-57.
[33]F Recknagel,M French,P Harkonen,KI Yabunaka.Artificial neural network approach for modelling and prediction of algal blooms[J].Ecological Modelling,1997,96(1-3):11-28.
[34]Yabunaka K.-i.,Hosomi M.,Murakami A.Novel application of a back-propagation artificial neural network model formulated to predict algal bloom[J].Water Science and Technology,1997,36(5):89-97(9).
[35]吴京洪,李锦蓉.人工神经网络预报浮游植物生长趋势的研究[J].中山大学学报(自然科学版),2000,39(6):54-58.
[36]Joseph H.W.Lee,Yan Huang,Mike Dickman and A.W.Jayawardena.Neural network modelling of coastal algal blooms[J].Ecological Modelling,2003,159(2-3):179-201.
[37]杨建强,高振会,孙培艳,石强,李钦亮.自组织神经网络在有害赤潮预警研究中的应用[J].海洋环境科学,2003,22(1):6-9.
[38]ZHANG Rong,YAN Hong,DU Liping.Research on Prediction of Red Tide Based on Fuzzy Neural Network[J].Marine Science Bulletin,2006,8(1):83-91.
[39]高强,宋神,杜忠晓.基于T-S模糊神经网络的信息融合在赤潮预测 预警中的应用[J].海洋技术,2006,25(2):103-106.
[40]H Wilson,F Recknagel.Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes[J].Ecological Modelling,2001,146(1-3):69-84.
[41]沈菲,曲阳,李会民,冯剑丰,王洪礼.浮游植物密度的人工神经网络预测研究[J].海洋技术,2005,24(1):44-47.
[42]Nitin Muttil,Kwok-Wing Chau.Neural network and genetic programming for modelling coastal algal blooms[J].International Journal of Environment and Pollution,2006,28(3/4):223-238.
[43]L.Velo-Suarez and J.C.Gutierrez-Estrada.Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva(Western Andalucia,Spain)[J].Harmful Algae,2007,6(3):361-371.
[44]Shen,Q.and Chouchoulas,A..FuREAP:a fuzzy-rough estimator of algae populations.Artificial Intelligence in Engineering,2001,15(1):13-24.
[45]陆文聪,杨柳等.支持向量机算法用于夜光藻密度建模[J].计算机与应用化学,2002,19(6):706-708.
[46]Qiuwen Chen,Arthur E.Mynett.Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake[J].Ecological Modelling,2003,162(1-2):55-67.
[47]S.Marsili-libelli,G.Pacini,C.Barresi.Fuzzy prediction of the algal blooms in the Orbetello lagoon.Environmental Modelling[J].Software,2004,19:799-808
[48]Fdez-Riverola,M.Corchado,J.M.Forecasting red tides using an hybrid neuro-symbolic system[J].AI COMMUNICATIONS,2003,16(4):221-234.
[49]Fdez-Riverola,M.Corchado.CBR based system for forecasting red tides[J].Knowledge-Based Systems,2003,16(5-6):321-328.
[50]Fdez-Riverola,M.Corchado.FSfRT:Forecasting System for Red Tides[J].Applied Intelligence,2004,21(3):251-264.
[51]Chen,Qiuwen;Mynett,Arthur.Forecasting Phaeocystis globosa blooms in the Dutch coast by an integrated numerical and decision tree model[J].Aquatic Ecosystem Health & Management,2006,9(3):357-364(8).
[52]冯剑锋,王洪礼,李胜朋.基于支持向量机的浮游植物密度预测研究[J].海洋环境科学,2007,26(5):438-441.
[53]K.W.Chau.Development of an integrated knowledge-based system on flow and water quality in Hong Kong coastal waters quality in Hong Kong coastal waters[J].International Journal of Environment and Pollution,2006,28(3-4):297-309.
[54]Kwokwing Chau.A review on the integration of artificial intelligence into coastal modeling[J].Journal of Environmental Management,2006,80(1):47-57.
[55]R Wang,V Storey,C Firth.A Framework for Analysis of Data Quality Research[J].IEEE Trans Knowledge and Data Engineering,1995,(7):623-640.
[56]Jiawei Han,Micheline Kamber.Data Mining:Concepts and Techniques[M].USA:Morgan Kaufmann Publishers,2001.
[57]Daubechies I.Where do wavelets come from?—A personal point of view[J].Proc.IEEE,1996,84(5):510-513.
[58]潘泉,张磊,孟晋丽,张洪才.小波滤波方法及应用[M].北京:清华大学出版社,2005.
[59]Michel Misit,Yves Misiti,et al.Wavelets and their applications [M].ISTE Publishing Company,2007.
[60]Tao Li,Qi Li,Shenghuo Zhu,Mitsunori Ogihara.A survey on wavelet applications in data mining[J].ACM SIGKDD Explorations Newsletter,2002,4(2):49-68.
[61]蒋鹏.小波理论在信号去噪和数据压缩中的应用研究[D].浙江大学,博士学位论文,2004.
[62]胡广书.数字信号处理-理论、算法与实现(第二版)[M].北京: 清华大学出版社,2003.
[63]Mallat S.,Hwang W.L..Singularity detection and processing with wavelets[J].IEEE Trans.Inform.Theory,1992,38(2):617-643.
[64]Xu Yansun,et al.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Trans.Image Processing,1994,3(6):747-758.
[65]Donoho D L.De-noising by soft-thresholding[J].IEEE Trans.Inform.Theory,1995,41(3):613-627.
[66]Donoho D L,Johnstone I M.,"Ideal spatial adaptation by wavelet shrinkage"[J].Biometrika,1994,81(3):425-455.
[67]Coifman R R,Donoho D L.Translation-invariant de-noising[J].Wavelets in Statistics of Lecture Notes in statistics.New York:Springer-Verlag,1994,103:125-150.
[68]Shensa,M.J.The discrete wavelet transform:wedding the a trous and Mallat algorithms[J].IEEE Trans.Signal Processing,1992,40(10):2462-2482.
[69]Lang M,et al.Noise reduction using an undecimated discrete wavelet transform[J].IEEE Signal Processing Letters,1996,3(1):10-12.
[70]Nason G P,Silverman B W.The stationary wavelet transform and some statistical applications[J].Lecture Notes in Statistics,1995,103:281-299.
[71]Fowler,J.E.The Redundant Discrete Wavelet Transform and Additive Noise[J].IEEE Signal Processing Letters,2005,12(9):629-632.
[72]Bruce A G.,Gao H Y.,"Understanding waveshrink:variance and bias estimation"[J].Biometrika,1996,83(4):727-745.
[73]安达六郎.赤潮生物と赤潮实态[J].水产土木,1973,9(1):31-36.
[74]夏综万,王寿松.大鹏湾的赤潮生态仿真模型[J].海洋与湖沼,1997,28(5):468-474.
[75]梁保松,曹殿立.模糊数学及其应用[M].北京:科学出版社,2007.
[76]Han,J.,Kamer,M.Data mining:Concepts and Techniques[M]. New York:Morgan-Kaufman,2007.
[77]吴今培,孙德山.现代数据分析[M].北京:机械工业出版社,2006.
[78]高新波.模糊聚类分析及其应用[M].西安:电子科技大学出版社,2004.
[79]Dave.R.N,Sen.S.Robust.Fuzzy Clustering of Relational Data[J].Fuzzy Systems,2002,10(6):713-727.
[80]Enrique H.Ruspini.A New Approach to Clustering[J].Information and Control,1969,15(1):22-32.
[81]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6):858-868.
[82]Rui Xu,Donald Wunsch Ⅱ.Survey of clustering algorithms[J].IEEE Trans.Neural Networks,2005,16(3):645-678.
[83]Dunn.J.C.,"A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters"[J],J.Cybernet.,1974,3(3):32-57.
[84]Dunn J.C.,"Well separated clusters and optimal fuzzy partitions".J.Cybern,1974,4:95-104.
[85]Bezedek JC.A convergence theorem for the fuzzy ISODATA clustering algorithms[J].IEEE Trans.on PAMI,1980,2(1):1-7.
[86]Bezdek,J.C.,Ehrlich,R.and Full,W..FCM:the fuzzy c-means clustering algorithm[J].Computer & Geosciences,1984,10:191-203.
[87]Jian Yu,Houkuan Huang.A New Weighting Fuzzy c-means Algorith[C].IEEE International Conference on Fuzzy System.2003:896-901.
[88]Jacek M.Leski.Generalized Weighted Conditional Fuzzy Clustering[J].IEEE TRANSACTIONS ON FUZZY SYSTEMS.2003,11(6):709-715.
[89]李洁,高新波,焦李成.基于特征加权的模糊聚类新算法[J].电子学报.2006,34(1):89-92.
[90]贾智平.嵌入式分布系统通信实时性问题的研究[D].山东大学,博士学位论文,2007.
[91] R.A.Fisher.. UCI repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1988.
[92] Robert Hecht-Nielsen. Applications of counterpropagation networks[J]. Neural Networks,1988, 1(2): 131-139 .
[93] MCCLLOCH W S, PITTS W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 10(5): 115-133.
[94] HEBB D O. The Organization of Behavior[M]. New York: Wiley, 1949.
[95] ROSENBLATTT F. The perception: A probabilistic model for information storage and organization in the brain[J] . Psychological Review,1958,65:386-408.
[96] Marvin Minsky, Seymour Papert. Perceptrons, An Introduction to Computational Geometry[M]. MIT Press, Cambridge, Mass., 1969.
[97] MCCLELLAND J L, RUMELHARD D E. Exploration in parallel distributed processing, A Handbook of Models, Programs, and Exercises[M]. Cambridge: MIT Press. 1986.
[98] Zhang Q, A. Benveniste. Wavelet Network[J]. IEEE Trans Neural Networks. 1992, 3(6): 889-898.
[99] BAHAVIK R B, STEPHANOPOULOS G. Wave-net: a muti-resolution, hierarchical neural network with localized learning[J] . AICHE. Journal, 1993, 9(1) : 57-82.
[100] Kreinovich, V., Sirisaengtaksin, O., Cabrera, S.. Wavelet Neural Networks are Asymptotically Optimal Approximators for Functions of One Variable[C]. Proc. of 1994 IEEE International conference on Neural networks, 1994,1: 299-304.
[101] Zhang Jun. Walter Gilber G, Wavelet Neural Network for Function Learning[J]. IEEE Trans on Signal Processing, 1995, 43(6): 1485-1497.
[102] Yamakawa T., Uchino E., Samatu T. The wavelet network using convex wavelets and its application to modeling dynamical systems[J].The Trans.on the IEICE.J79-A.12,1996,P.2046-2053.
[103]Zhang Q.Using wavelet network in nonparametric estimation[J].IEEE Transactions on Neural Networks.,1997,8(2):227-236
[104]JIAO L C,PAN J,FANG Y W.,Multiwavelet neural network and its approximation properties[J].IEEE Transactions on Neural Networks.,2001,12(5):1060-1066.
[105]张茁生,刘贵忠,刘峰.一种自适应小波网络的构造及其学习算法[J].中国科学(E辑),2001,31(2):172-181.
[106]丁勇,刘守生,胡寿松.一种广义小波神经网络的结构及其优化方法[J].控制理论与应用,2003,20(1):125-128.
[107]岑翼刚,秦元庆,孙德宝,李宁.粒子群算法在小波神经网络中的应用[J].系统仿真学报,2004,(12):2783-2785(2788)
[108]StephenA.Billings,Hua-LiangWei.,A New Class Of Wavelet Networks For Nonlinear System Identification[J].IEEE Transactions on Neural Networks.,2005,16(4):862-874.
[109]Hojjat Adeli,F.ASCE and Xiaomo Jiang.Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification[J].Journal of Structural Engineering,2006,132(1):102-111.
[110]李俊民,李靖,薄立军.一类非线性动态系统的自适应模糊小波神经网络控制[J].工程数学学报,2006,23(1):37-44.
[111]Ping Guo,Hongzhai Li.Blind Image Restoration by Combining Wavelet Transform and RBF Neural Network[J].International Journal of Wavelets,Multiresolution and Information Processing,2007,5(1):15-26.
[112]Victor M.Becerra,Roberto K.Galvao,Magda Abou-Seada,Neural and Wavelet Network Models for Financial Distress Classification[J],Data Mining and Knowledge Discovery,2005,11(1):35-55.
[113]K.Vinay Kumar,V.Ravi,Mahil Carr,N.Raj Kiran,Software development cost estimation using wavelet neural networks[J].Journal of Systems and Software,2008,81(11):1853-1867.
[114]Serdar HEKIMHAN,Serdar MENEKAY,N.Serap S_ENGOR Prior Knowledge Input Method in Device Modeling[J].Turk J Elec Engin,2005.13(1):51-60.
[115]侯霞.小波神经网络若干关键问题研究[D].南京航天航空大学,博士学位论文,2006.
[116]Lindsay I Smith.A tutorial on Principal Components Analysis.http://csnet.otago.ac.nz/cosc453/student_tutorials/,2002.
[117]Shlens J.,A Tutorial on Pricipal Component analysis.http://www.snl.salk.edu/～shlens/,2005.
[118]张晓华,吴贵强.基于主元分析的传感器故障诊断[J].武汉理工大学学报(信息与管理工程版),2002,24(2):57-59.
[119]赵成燕,赵刚,刘爱伦.基于改进PCA的故障检测与诊断方法[J].石油化工自动化,2006,1:41-44.
[120]Aggarwal C,Yu P.Finding Generalized Projected Clusters in Dimensional Spaces[C]//Proc.of ACM SIGMOD Conference on Management Data.Dallas,Texas,U.S.A:[s.n.],2000:78-52.
[121]Tsai Chengfa,Tsai Chunwei,Chen Chiping.A Novel Multiple Searching Genetic Algorithm for Multimedia Multicast Routing[C].Proc.of IEEE Congress on Evolutionary Computation.Piscataway,NJ:[s.n.],2002:7065-7068.
[122]Davidson I,Satyanarayana A.Speeding Up K-means Clustering by Bootstrap Averaging[C].Proc.of IEEE Data Mining Workshop on Clustering Large Data Sets.Brighton,UK:[s.n.],2004:98-102.
[123]LukaszA.Kurgan,Petr Musilek.Asurvey of Knowledge Discovery and Data Mining process models[J].The Knowledge Engineering Review,2006,21(1):1-24.
[124]韩家炜.数据挖掘概念与技术[M].北京:机械工业出版社,2001.
[125]Margaret H.Dunham.数据挖掘教程[M].郭崇慧,田凤占,靳晓明.北京:清华大学出版社,2005.
[126]绍峰晶,于忠清.数据挖掘—原理与算法[M].北京:中国水利水电出版社,2003.
[127]毛国君,段立娟,王实,石云.数据挖掘原理与算法[M].北京:清华大学出版社,2005.
[128]Michael Goebel,Le Gruenwald.A survey of data mining and knowledge discovery software tools[J].SIGKDD Explorations,1999,1(1):20-33.
[129]恽爽,胡南军,董浚等.数据挖掘软件研究现状[J].计算机工程与应用,2003,08:189-192.
[130]李伟.软件架构的艺术[M].北京:电子工业出版社,2008.
[131]Michael S.Mimoso.A defining moment for SOA[EB/OL].[2004].http://searchsoa.techtarget.com/news/article/0,289142,sid26_gci1017004,00.html.
[132]Ali Arsanjani.Service-oriented modeling and architecture[EB/OL].[2004].http://www.ibm.com/developerworks/webservices/library/ws-soa-design1/?S_TACT=105AGX52&S_CMP=cn-a-ws.
[133]David J.N.Artus.SOA实现:服务设计原则[EB/OL].[2006].http://www.ibm.com/developerworks/cn/webservices/ws-soa-design/.
[134]Oasis.Reference Model for Service Oriented Architecture 1.0[EB/OL].[2006].http://www.oasis-open.org/committees/soa-rm/.
[135]CHEN Ying,COHEN B..Data mining and service rating in serviceoriented architectures to improve information sharing[C].Proc of IEEE,Aerospace Conference.2005:1-11.
[136]杨立,左春,王裕国.面向服务的知识发现体系结构研究与实现[J].计算机学报,2005,28(4):445-457.
[137]贾西平,彭宏,郑启伦.面向服务的数据挖掘平台体系结构研究[J].计算机应用研究,2008,25(2):440-442.
[138]任磊,王威信等.一种模型驱动的交互式信息可视化开发方法[J].软件学报,2008,19(8):1947-1964.
[139]Baron,M.et Girard,P.SUIDT.A task model based GUI-Builder[C].In Proceedings of TAMODIA:Task MOdels and DIAgrams for user interface design(July 18-19,2002,Romania,Bucharest),Inforec Printing House,2002,pp.64-71.
[140]余世银,乐嘉锦.可视化数据挖掘模型研究[J].计算机科学,2000,10(增刊):110-113.
[141]姬鹏.有害藻华监测与预报系统研究[D].西安科技大学,硕士学位论文,2006.
[142]宁勇.数据挖掘在海洋环境在线监测及赤潮灾害智能预警系统中的应用[D].山东大学,硕士学位论文,2008.