多波束测深数据质量控制方法研究
|
摘要
本文对多波束测深数据质量控制的方法进行了较为系统和全面的研究。内容涵盖多波束测深系统误差源分析、多波束测深系统的声速改正技术、多波束测深异常值的探测技术、基于最小二乘支持向量机构建海底趋势面的算法研究、优化测深训练样本对海底趋势面构造的影响分析、多波束测深系统误差消除算法研究等等。取得的主要成果和创新点概括如下:
1.针对传统经验正交函数分析方法在声速剖面构造方面存在的不足,提出了声速剖面经验模分解的方法,给出了修正后的声速剖面时间函数和空间函数,为测量海区声速变化模型的构建提供了必要的参数。通过实际观测数据的计算分析,有效验证了利用经验模分解的方法重构声速剖面的可行性和正确性。
2.详细分析了多波束测深异常值产生的原因,并以此为基础对几种常见的多波束测深异常值探测方法从基本原理、适用条件、优缺点等方面进行了比较。结合遗传算法在异常值探测方面的优越性,提出了相应的测深异常值探测方法。根据遗传算法的择优选取原则,给出了测深异常值的探测步骤。实测算例表明基于遗传算法构建的异常值探测模型可以快速有效地标定出测深异常值。
3.多波束测深是一个动态的过程,采集得到的水深数据具有不可重复性,针对这一特点,将Bayes估计理论和MCMC抽样设计方法引入到测深异常值的探测中。以水深观测数据为研究对象,详细推导了利用Bayes估计探测水深异常值的公式,并给出了详细的探测步骤。通过实际观测数据的计算分析,表明利用Bayes估计构造的算法能以简单明了的判断准则对测深异常值进行探测。
4.深入研究最小二乘支持向量机算法的基本原理,并结合该算法在自由曲面重构方面的功能,将该算法引入到海底趋势面的构造中,并在此基础上对测深异常值进行探测;结合多波束测深数据海量性的特点,提出了有效的训练样本选取方案;给出了不同核函数的数学表达式,并进一步分析了不同的核函数对海底趋势面构造的影响;仿真算例表明为了避免海底微小地形的丢失,应结合海底地形的实际情况选取不同的核函数对海底趋势面进行构造。
5.在分析不同核函数对海底趋势面构造影响的基础上,推导证明了趋势面滤波与最小二乘支持向量机算法在特定条件下的等价性,给出了公式推导过程;结合海底实际变化情况,提出了不同海底类型适用的核函数。利用实测数据对上述两种方法进行了分析和比较,得出在多项式核函数的阶数等于趋势面滤波的阶数,且权重系数等于0.5的情况下,利用最小二乘支持向量机构造的函数等价于趋势面滤波。
6.最小二乘支持向量机在海底趋势面构造过程中无法消除较大偏差训练样本的影响,针对该缺陷,提出局部样本中心距优化测深训练样本的方法,从而降低了非正常训练样本对海底趋势面构造的影响。实测算例表明利用局部样本中心距能合理地优化测深训练样本,从而能合理地构造出海底趋势面,异常值也能得到有效地剔除。
7.对多波束测深的各个误差源进行了定量的分析,给出了水深不确定度的计算公式;由于水深不确定度反映了测量水深的分散性程度,因此将其引入到测深训练样本的优化中,从而选取出对海底趋势面构造贡献率大的测深训练样本。实测算例表明利用不确定度能合理地优化训练样本,进而合理地构造出海底趋势面,异常值也能得到有效地剔除。
8.详细地分析了多波束测深系统误差产生的原因,传统的两步滤波法虽然能有效地解决重叠区内测深数据的不匹配问题,但是并未顾及重叠区外的测深数据。针对该不足提出了基于不确定度的测深系统误差消除算法。实测算例表明利用不确定度修正的两步滤波法不仅能解决相邻条带重叠区内测深数据的不匹配问题,而且能顾及到重叠区外的测深数据,避免了海底地形发生扭曲。
This dissertation mainly focuses on the method of multibeam echosounding data quality control which includes the principles of multibeam echosounding, the errors analysis, correction technology of sound velocity profiles for multibeam survey, detection technology of outliers for multibeam survey,research on constructing trend surface by LS-SVM, the influence of optimized train samples on elimination of sounding outliers in the LS-SVM arithmetic, correction technology of system errors for multibeam survey. The main works and contributions are summarized as follows:
1. In the process of sound velocity profile conformation, the traditional empirical orthogonal function analysis method has some shortage, so a new approach based on empirical mode decomposition is presented. The orthogonal time functions and spatial functions of sound velocity profile are given. The new method supplies the essential parameters for the model conformation of survey area. By the calculation analysis of the actual observation data, the new method based on empirical mode decomposition could reconstruct sound velocity profile effectively and accurately.
2. The cause of Multibeam echosounding outliers is analysed and a comparion of several common multibeam echosounding outliers’detection methods from the basic principle, the applicable condition, etc is given. Due to the abnormal value detection superiority of the genetic algorithm, the article puts forward the corresponding sounding outliers’detection method. Base on the selected principles of genetic algorithm, the steps for detecting outliers of sounding is given. The actual example shows that the model based on genetic algorithm could detect outliers quickly and effectively.
3. Multibeam echosounding is a dynamic process; the depth is not repeating data. Aiming at the character; the Bayes estimation theory and MCMC sampling design method are imported to detecting outliers. Take the depth observation data as the research object, the formula of outliers detection be deduced by the Bayes estimation theory, and the steps of the detection is given; The actual calculation shows that the sounding abnormal value detection methods based on Bayes estimation theory could gain higher accuracy by simple judgment criterion.
4. Further research the least squares support vector machine method, recur to the function of the free surface reconstruction, the ls-svm algorithm is imported to constructing the undersea trend surface, and the outliers could be detected by the trend surface.Multibeam echosounding data with the characteristics of mass, so the training sample selection plan is presented; Different kernel functions are given, and the influence of different kernel functions on seafloor trend surface are analyzed; Simulation examples show that in order to avoid the loss of seabed tiny terrain, should combine the actual situation of seabed terrain then choose the correct kernel function.
5. On the basis of the influence of different kernel functions on seafloor trend surface structure, the equivalence between trend surface filtering and least squares support vector machine method is proved, and the formulas derivation is given; Combined with the actual situation, different kernel functions are suited for corresponding sea area; A comparion between trend surface filtering and least squares support vector machine method is given.The conclusion is that trend surface filtering is the especial result for least squares support vector machine method when the weight parameter equal to 0.5.
6. In the process of sea floor trend surface construction, least squares support vector machines algorithm cannot eliminate the influence of large deviation training samples, aiming at this defect, the article puts forward local sample center distance method.The method could optimize sounding training samples, reduce the influence of abnormal training samples. The actual example shows that sounding training samples could be optimized reasonably by the local sample center distance, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.
7. The quantitative analysis is given about the multibeam echosounding error sources, the formulas of the depth uncertainty is obtained; Because of the uncertainty reflects the dispersion of the water depth measurement, the article import it to optimize the sounding training samples. The large contribution rate of the training samples should be chosed by the method. Real calculational results show that the sounding training samples could be optimized reasonably, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.
8. Analyzes the cause of system error in the process of multibeam echosounding, although traditional two-step filtering method can solve the problem effectively in overlapping area sounding data don't match, did not attend the other sounding data. Aiming at the shortage, algorithm about eliminating sounding system error based on uncertainty is presented. The actual example shows that the improved two-step filtering method considers the other sounding data to avoid the seabed terrain distortions.
1.针对传统经验正交函数分析方法在声速剖面构造方面存在的不足,提出了声速剖面经验模分解的方法,给出了修正后的声速剖面时间函数和空间函数,为测量海区声速变化模型的构建提供了必要的参数。通过实际观测数据的计算分析,有效验证了利用经验模分解的方法重构声速剖面的可行性和正确性。
2.详细分析了多波束测深异常值产生的原因,并以此为基础对几种常见的多波束测深异常值探测方法从基本原理、适用条件、优缺点等方面进行了比较。结合遗传算法在异常值探测方面的优越性,提出了相应的测深异常值探测方法。根据遗传算法的择优选取原则,给出了测深异常值的探测步骤。实测算例表明基于遗传算法构建的异常值探测模型可以快速有效地标定出测深异常值。
3.多波束测深是一个动态的过程,采集得到的水深数据具有不可重复性,针对这一特点,将Bayes估计理论和MCMC抽样设计方法引入到测深异常值的探测中。以水深观测数据为研究对象,详细推导了利用Bayes估计探测水深异常值的公式,并给出了详细的探测步骤。通过实际观测数据的计算分析,表明利用Bayes估计构造的算法能以简单明了的判断准则对测深异常值进行探测。
4.深入研究最小二乘支持向量机算法的基本原理,并结合该算法在自由曲面重构方面的功能,将该算法引入到海底趋势面的构造中,并在此基础上对测深异常值进行探测;结合多波束测深数据海量性的特点,提出了有效的训练样本选取方案;给出了不同核函数的数学表达式,并进一步分析了不同的核函数对海底趋势面构造的影响;仿真算例表明为了避免海底微小地形的丢失,应结合海底地形的实际情况选取不同的核函数对海底趋势面进行构造。
5.在分析不同核函数对海底趋势面构造影响的基础上,推导证明了趋势面滤波与最小二乘支持向量机算法在特定条件下的等价性,给出了公式推导过程;结合海底实际变化情况,提出了不同海底类型适用的核函数。利用实测数据对上述两种方法进行了分析和比较,得出在多项式核函数的阶数等于趋势面滤波的阶数,且权重系数等于0.5的情况下,利用最小二乘支持向量机构造的函数等价于趋势面滤波。
6.最小二乘支持向量机在海底趋势面构造过程中无法消除较大偏差训练样本的影响,针对该缺陷,提出局部样本中心距优化测深训练样本的方法,从而降低了非正常训练样本对海底趋势面构造的影响。实测算例表明利用局部样本中心距能合理地优化测深训练样本,从而能合理地构造出海底趋势面,异常值也能得到有效地剔除。
7.对多波束测深的各个误差源进行了定量的分析,给出了水深不确定度的计算公式;由于水深不确定度反映了测量水深的分散性程度,因此将其引入到测深训练样本的优化中,从而选取出对海底趋势面构造贡献率大的测深训练样本。实测算例表明利用不确定度能合理地优化训练样本,进而合理地构造出海底趋势面,异常值也能得到有效地剔除。
8.详细地分析了多波束测深系统误差产生的原因,传统的两步滤波法虽然能有效地解决重叠区内测深数据的不匹配问题,但是并未顾及重叠区外的测深数据。针对该不足提出了基于不确定度的测深系统误差消除算法。实测算例表明利用不确定度修正的两步滤波法不仅能解决相邻条带重叠区内测深数据的不匹配问题,而且能顾及到重叠区外的测深数据,避免了海底地形发生扭曲。
This dissertation mainly focuses on the method of multibeam echosounding data quality control which includes the principles of multibeam echosounding, the errors analysis, correction technology of sound velocity profiles for multibeam survey, detection technology of outliers for multibeam survey,research on constructing trend surface by LS-SVM, the influence of optimized train samples on elimination of sounding outliers in the LS-SVM arithmetic, correction technology of system errors for multibeam survey. The main works and contributions are summarized as follows:
1. In the process of sound velocity profile conformation, the traditional empirical orthogonal function analysis method has some shortage, so a new approach based on empirical mode decomposition is presented. The orthogonal time functions and spatial functions of sound velocity profile are given. The new method supplies the essential parameters for the model conformation of survey area. By the calculation analysis of the actual observation data, the new method based on empirical mode decomposition could reconstruct sound velocity profile effectively and accurately.
2. The cause of Multibeam echosounding outliers is analysed and a comparion of several common multibeam echosounding outliers’detection methods from the basic principle, the applicable condition, etc is given. Due to the abnormal value detection superiority of the genetic algorithm, the article puts forward the corresponding sounding outliers’detection method. Base on the selected principles of genetic algorithm, the steps for detecting outliers of sounding is given. The actual example shows that the model based on genetic algorithm could detect outliers quickly and effectively.
3. Multibeam echosounding is a dynamic process; the depth is not repeating data. Aiming at the character; the Bayes estimation theory and MCMC sampling design method are imported to detecting outliers. Take the depth observation data as the research object, the formula of outliers detection be deduced by the Bayes estimation theory, and the steps of the detection is given; The actual calculation shows that the sounding abnormal value detection methods based on Bayes estimation theory could gain higher accuracy by simple judgment criterion.
4. Further research the least squares support vector machine method, recur to the function of the free surface reconstruction, the ls-svm algorithm is imported to constructing the undersea trend surface, and the outliers could be detected by the trend surface.Multibeam echosounding data with the characteristics of mass, so the training sample selection plan is presented; Different kernel functions are given, and the influence of different kernel functions on seafloor trend surface are analyzed; Simulation examples show that in order to avoid the loss of seabed tiny terrain, should combine the actual situation of seabed terrain then choose the correct kernel function.
5. On the basis of the influence of different kernel functions on seafloor trend surface structure, the equivalence between trend surface filtering and least squares support vector machine method is proved, and the formulas derivation is given; Combined with the actual situation, different kernel functions are suited for corresponding sea area; A comparion between trend surface filtering and least squares support vector machine method is given.The conclusion is that trend surface filtering is the especial result for least squares support vector machine method when the weight parameter equal to 0.5.
6. In the process of sea floor trend surface construction, least squares support vector machines algorithm cannot eliminate the influence of large deviation training samples, aiming at this defect, the article puts forward local sample center distance method.The method could optimize sounding training samples, reduce the influence of abnormal training samples. The actual example shows that sounding training samples could be optimized reasonably by the local sample center distance, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.
7. The quantitative analysis is given about the multibeam echosounding error sources, the formulas of the depth uncertainty is obtained; Because of the uncertainty reflects the dispersion of the water depth measurement, the article import it to optimize the sounding training samples. The large contribution rate of the training samples should be chosed by the method. Real calculational results show that the sounding training samples could be optimized reasonably, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.
8. Analyzes the cause of system error in the process of multibeam echosounding, although traditional two-step filtering method can solve the problem effectively in overlapping area sounding data don't match, did not attend the other sounding data. Aiming at the shortage, algorithm about eliminating sounding system error based on uncertainty is presented. The actual example shows that the improved two-step filtering method considers the other sounding data to avoid the seabed terrain distortions.
引文
[1]曹洪泽,朱琦,徐新盛.条带测深系统中使用多子阵的海底探测技术[J].交通运输工程学报,2002,2(4):116-120.
[2]曹玲,窦永祥.黑河流域降水的时空特征及预报方法[J].干旱气象,2005,23(2):35-38.
[3]陈将宏,周德强,李落清.加权最小二乘支持向量机与最小二乘问题的关系[J].湖北大学学报.自然科学版,2004,26(1):16-18.
[4]陈磊,张土乔.基于最小二乘支持向量机的时用水量预测模型[J].哈尔滨工业大学学报,2006,38(9):1528-1530.
[5]陈上及,马继瑞.海洋数据处理分析方法及其应用[M].北京:海洋出版社,1991.
[6]陈伟,胡昌华,曹小平等.基于最小二乘支撑矢量机的陀螺仪漂移预测[J].宇航学报,2006,27(1):135-138.
[7]陈效逑,彭嘉栋,李慧敏.内蒙古地区气温变化的季节和区域差异[J].地理研究,2009,28(1):27-35.
[8]陈友元.便携式多波束测深仪实时测控与数据可视技术研究[D].哈尔滨工程大学,2005.
[9]陈隽,徐幼麟,李杰. Hilbert—Huang变换在密频结构阻尼识别中的应用[J].地震工程与工程振动,2003,23(4):34-42.
[10]陈隽,徐幼麟,李杰.经验模分解及小波分析在结构损伤识别中的应用:试验研究[J].地震工程与工程振动,2007,27(1):110-116.
[11]丁继胜,周兴华,吴永亭,胡光海.多波束回声测深系统测量数据的分离提取方法[J].海洋测绘,2006,26(4):33-35.
[12]丁继胜,周兴华,唐秋华,陈义兰.多波束勘测声速剖面场的EOF表示方法[J].武汉大学学报.信息科学版,2007,32(5):446-449.
[13]董江,任立生.基于趋势面的多波束测深数据滤波方法[J].海洋测绘,2007,27(6):25-28
[14]董庆亮,韩红旗,方兆宝,潘乐等.声速剖面改正对多波束测深的影响[J].海洋测绘,2007,27(2):56-58.
[15]董西国.支持向量机在数据挖掘中的应用[D].哈尔滨工程大学, 2002.
[16]董云杰.支持向量机及其在模式识别中应用的研究[D].兰州理工大学, 2003.
[17]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2006.
[18]邓兴升,花向红.动态最小二乘支持向量机学习算法[J].武汉大学学报.信息科学版,2008,33(11):1122-1125.
[19]杜树新,吴铁军.模式识别中的支持向量机方法[J].浙江大学学报(工学版),2003,37(5):521-527.
[20]范震寰.多波束测深关键技术研究[D].南京航空航天大学,2005.
[21]封筠,陈志军,李莉蓉.基于修正核函数的SVM分类器研究[J].系统仿真学报, 2006,18(3):570-572.
[22]封筠,颉斌,郝卫东,杨扬.一种SVM分类器自动模型选择方法[J].北京科技大学学报,2006,28(1):88-92.
[23]傅佩红,李雪飞,贺立源.遗传算法在特异值识别与处理中的应用[J].测绘科学,2008,33(2):192-194.
[24]高金耀,金翔龙,吴自银.多波束数据的海底数字地形模型构建[J].海洋通报,2003,22(1):30-38.
[25]高金耀,方银霞,徐赛英.原始多波束数据的格式转换与统一[J].海洋通报,2002,21(6):68-74.
[26]郭发滨,周兴华,陈义兰.多波束测深数据出现失真的因果分析[J].海洋测绘,2008,28(1):59-61.
[27]郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究[J].系统仿真学报,2006,18(7):2033-2035.
[28]郭辉,王玲,刘贺平.基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题[J].北京科技大学学报,2006,28(3): 303-306.
[29]郭辉,刘贺平,王玲.基于最小二乘支持向量机对偶优化问题的核偏最小二乘[J].北京科技大学学报,2006,28(8):790-793.
[30]韩梅,魏亮,周艳霞.改进型经验正交函数海洋声速剖面预报方法[J].海洋科学,2009,33(1):30-33.
[31]何高文.多波束测深系统声速校正[J].海洋技术,2000,19(4):14-21.
[32]何义斌,吴书帮,谢洪燕,赵建虎.多波束异常测深数据检测方法实践[J].测绘科学,2004,29(1):50-52.
[33]侯世喜,黄谟涛,欧阳永忠,陆秀平等.多波束数据处理软件的应用与启示[J].海洋测绘,2003,23(6):14-17.
[34]胡国胜,钱玲,张国红.支持向量机的多分类算法[J].系统工程与电子技术,2006,28(1):127-132.
[35]胡清华,张建华,李海森,魏玉阔.利用MB-system软件进行多波束测深数据处理的研究[J].海洋测绘,2006,26(5):28-30.
[36]黄谟涛,翟国君,欧阳永忠,刘雁春,陆秀平,王克平.多波束与单波束测深数据的融合处理技术[J].测绘学报,2001,30(4):299-303.
[37]黄谟涛,翟国君,管铮等.多波束测深技术研究进展与展望[A].第十二届海洋测绘综合性学术研讨会,2001:3-8.
[38]黄贤源,翟国君,隋立芬,黄谟涛,欧阳永忠,柴洪洲. LS-SVM算法中优化训练样本对测深异常值剔除的影响[J].测绘学报,2011,40(1):22-27.
[39]黄贤源,隋立芬,翟国君,柴洪洲,王海栋.基于Bayes估计的多波束测深异常数据探测方法[J].武汉大学学报.信息科学版,2010,35(2):168-171.
[40]黄贤源,翟国君,隋立芬,黄谟涛等.最小二乘支持向量机在海洋测深异常值探测中的应用[J].武汉大学学报.信息科学版,2010,35(10):1188-1191.
[41]黄贤源,翟国君,黄谟涛,隋立芬,柴洪洲,陆秀平.利用水深不确定度探测测深异常值的方法[J].测绘科学技术学报,2011,28(1):70-74.
[42]黄贤源,隋立芬,翟国君,柴洪洲等.遗传算法在海洋测深异常数据处理中的应用[J].测绘科学技术学报,2010,27(4):247-250.
[43]黄贤源,翟国君,黄谟涛,隋立芬.最小二乘支持向量机构造海底趋势面的论证研究[J].海洋测绘,2010,30(3):9-12.
[44]黄贤源,隋立芬,王威,牟忠凯.有色噪声条件下野值的辨识与处理[J].辽宁工程技术大学学报.信息科学版,2010,29(2):212-215.
[45]季娟,姚恩涛,张明,刘兼唐.基于HHT的汽车动态称重数据分析方法[J].数据采集与处理,2006,21(3):340-344.
[46]江力,李长云.基于经验模分解的小波阈值滤波方法研究[J].信号处理,2005,21(6):659-662.
[47]孔锐,张冰.一种快速最小二乘支持向量机分类算法[J].计算机工程与应用,2007,43(32):167-170.
[48]兰光华.支持向量机训练算法实现及其改进[D].南京理工大学,2005.
[49]赖仲淄,陈坚雄,郑道贤,潘剑中,吴宏旭.SEABEAM1185多波束系统的测量精度分析及在堤防监测中的应用[J].海洋测绘,2007,27(6):58-61.
[50]李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报(工学版),2006,40(2):202-206.
[51]李海森,黎子盛,周天,孙圣和.MSB算法在多波束测深系统中的应用[J].声学技术,2007,26(2):286-290.
[52]李家彪,王小波,华祖根等.多波束勘查原理技术与方法[M].北京:海洋出版社,1999 ,197-207.
[53]李家彪,郑玉龙,王小波,吴自银.多波束测深及影响精度的主要因素[J].海洋测绘,2001,(1):26-32.
[54]李凯,郭子雪.基于SVM的函数模拟[J].河北大学学报(自然科学版),2001,21(1):7-8.
[55]李莉丽.浅水多波束条带测深仪实时显控系统技术的研究[D].哈尔滨工程大学,2005.
[56]李琳,张晓龙.支持向量机学习方法的选择与应用[J].武汉科技大学学报(自然科学版), 2006,29(1):75-78.
[57]李天云,王爱凤,程思勇,吴正非.用LS-SVM分析油浸式变压器故障[J].高电压技术,2008,34(2):346-349.
[58]李文杰,胡平,肖都,刘春雷.多波束测深在海洋工程勘察中的应用[J].物探与化探,2004,28(4):373-376.
[59]李新娜,归庆明,许阿裴.基于识别变量的粗差探测Bayes方法[J],测绘学报,2008,37(3):355-360.
[60]李新娜.多个粗差探测的Bayes方法与应用[D].解放军信息工程大学,2008:15-25.
[61]李宜龙,李岳和,许军.水位观测数据粗差探测技术研究[M],中国测绘学会海洋测绘专业委员会第二十届海洋测绘综合性学术研讨会论文集,2008,:39-40.
[62]李治友.遗传算法和支持向量机混合方法及其应用[D].重庆大学,2003.
[63]刘汉军.多波束全景扫描声呐设计与实现[D].中国科学院声学研究所,2002.
[64]刘胜旋,关永贤.多波束系统的参数误差判断及校正[J].海洋测绘,2002,22(1):33-37.
[65]刘方兰,张志荣,余平.多波束系统横摇、纵倾参数的校正方法[J].吉林大学学报(地球科学版),2004,34(4):621-624.
[66]刘小茂,孔波,高俊斌,张钧.一种稀疏最小二乘支持向量分类机[J].模式识别与人工智能,2007,20(5):681-687.
[67]刘雁春,肖付民,暴景阳,徐卫明.海道测量学概论[M].北京,测绘出版社,2006.
[68]刘雁春.海洋测深空间结构及其数据处理[D].武汉:武汉测绘科技大学,1998.
[69]刘志刚,秦前清,李德仁,王新洲.基于混合核函数的支撑向量机及其在遥感影像土地利用分类中的应用[J].测绘信息与工程, 2003,28(5):1-3.
[70]鹿卫国,戴亚平,涂序彦,高峰.适用于加权样本集处理的加权支持向量机方法[J].北京理工大学学报,2005,25(3):211-215.
[71]陆秀平,黄谟涛,翟国君,欧阳永忠,徐广袖,王克平.多波束测深数据处理及管理系统设计与开发[J].海洋测绘,2006,26(6):1-5.
[72]骆剑承,周成虎,梁怡,马江洪.支撑向量机及其遥感影像空间特征提取和分类的应用研究[J].遥感学报,2002,6(1):50-55.
[73]罗深荣.侧扫声纳和多波束测深系统在海洋调查中的综合应用[J].海洋测绘,2003,23(1):22-24.
[74]吕海龙,杜德文,刘炎光,杨刚,田丽艳,闫仕娟.多波束回声数据的统计与底质分类应用[J].海洋科学进展,2006,24(4):463-471.
[75]吕剑峰,戴连奎.加权最小二乘支持向量机改进算法及其在光谱定量分析中的应用[J].分析化学,2007,35(3):340-344.
[76]马开银.小型化多波束条带测深系统研究[D].哈尔滨工程大学,2002.
[77]马建林,金菁,刘勤,来向华.多波束与侧扫声纳海底目标探测的比较分析[J].海洋测绘,2006,26(3):10-12.
[78]马建林,金菁,来向华.多波束测深海底数字地形模型的建立[J].海洋测绘,2005,25(5):15-17.
[79]马文亮,卢秋芽.多波束在海底复杂地形条件下大比例尺测量方法探讨[J].上海地质,2006,(1):19-22.
[80]梅建新,段汕,秦前清.基于支持向量机的特定目标检测方法[J].武汉大学学报.信息科学版,2004,29(10):912-915.
[81]聂良春,朱琦.多波束测深相位检测法中一种有效的相位差序列提取方法[J].舰船科学技术,2003,25(6):3-5.
[82]聂良春,朱琦,李海森.幅度-相位联合检测法在多波束测深系统中的应用[J].声学技术,2005,24(2):84-88.
[83]聂良春.多波束相位差法海底TOA估计理论分析及实用研究[D].哈尔滨工程大学,2003.
[84]乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方法[J].电子学报, 2006,34(3):496-498.
[85]阮锐,邵海涛.多波束测深系统内部参数的检测与分析[J].海洋测绘,2001,(4):51-54.
[86]沈斌,姚敏,易文晟.基于最小二乘支持向量机的模糊时序分析方法[J].浙江大学学报(工学版),2005,39(8):1142-1146.
[87]舒忠平,杨智春.抑制经验模分解边缘效应的极值点对称延拓法[J].西北工业大学学报,2006,24(5):639-643.
[88]宋平舰,张杰.二维经验模分解在海洋遥感图像信息分离中的应用[J].高技术通讯,2001,(9):62-67.
[89]宋志宇,李俊杰.最小二乘支持向量机在大坝变形预测中的应用[J].水电能源科学,2006,24(6):49-52.
[90]孙海,陈伟,陈隽.强风环境非平稳风速模型及应用[J].防灾减灾工程学报,2006,26(1):52-57.
[91]孙岚,刘雁春,陆秀平,欧阳永忠,李明叁,许家琨.利用CUBE算法处理多波束测深数据研究[A].第二十一届海洋测绘综合性学术研讨会,2009:122-126.
[92]谭琨,杜培军,郑辉.支持向量机在空间信息处理领域的应用研究[J].测绘科学,2007,32(2):87-89.
[93]谭小彬,奚宏生,王卫平,殷保群.基于支持向量机的异常检测[J].中国科学技术大学学报,2003,33(5):599-605.
[94]王冬梅,吴杰.基于多波束测深系统的三维河势分析[J].水利水电技术,2004,35(2):92-93.
[95]王国胜,钟义信.支持向量机的若干新进展[J].电子学报,2001,29(10):1397-1400.
[96]王海栋.多波束系统测深异常处理理论与方法研究[D].中国人民解放军信息工程大学,2010.
[97]王红军,徐小力.支持向量机在设备故障诊断方面的应用研究概述[J].机械设计与制造,2005,(9):157-159.
[98]王琪,刘雁春,暴景阳.关于多波束与侧扫声纳回波检测方法的探讨[J].海洋技术,2003,22(3):70-73.
[99]王闰成,卫国兵.多波束探测技术的应用[J].海洋测绘,2003,23(5):20-23.
[100]王伟,魏洪兴,陈殿生,王田苗.基于经验模分解和最小二乘支持矢量机的装载机载质量动态测量混合建模方法[J].机械工程学报,2008,44(2):87-93.
[101]王向红.多波束条带测深系统数据后置处理技术研究[D].哈尔滨工程大学,2005.
[102]王新晓,黄建国,褚福照,张永峰.数字多波束目标被动定向仿真技术研究[J].应用声学,2004,23(6):31-34.
[103]魏凤英.现代气候统计诊断与预测技术[M].北京:气象出版社,2007.
[104]魏凤英,冯蕾,马玉平,王石立.东北地区玉米气候生产潜力时空分布特征[J].气象科技,2010,38(2):243-247.
[105]吴少琴.多波束条带测深仪拼图技术的研究[D].哈尔滨工程大学,2003.
[106]吴英姿,徐新盛,乔力争.多波束测深系统的精度评估方法研究[J].海洋技术,2003,22(3):65-69.
[107]吴英姿.多波束测深系统地形跟踪与数据处理技术研究[D].哈尔滨工程大学,2001.
[108]吴自银,李家彪.多波束勘测的数据编辑方法[J].海洋通报,2000,19(3):74-78.
[109]吴自银,金翔龙,郑玉龙,李家彪,余平.多波束测深边缘波束误差的综合校正[J].海洋学报,2005,27(4):88-94.
[110]吴兆福,宫鹏,高飞,王侬.基于支持向量机的GPS似大地水准面拟合[J].测绘学报,2004,33(4):303-306.
[111]肖付民,暴景阳,吕仁臣.多波束坐标系统及误差源分析[J].海洋测绘,2001,(2):41-44.
[112]熊学军,郭炳火,胡筱敏,刘建军.EMD方法和Hilbert谱分析法的应用与探讨[J].黄渤海海洋,2002,20(2):12-21.
[113]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策,2004,19(5):481-484.
[114]许新发,樊宜.SeaBat8101多波束测深系统及其应用[J].江西水利科技,2001,27(4):211-214.
[115]阎辉,张学工,李衍达.支持向量机与最小二乘法的关系研究[J].清华大学学报.自然科学版,2001,41(9):77-80.
[116]阳凡林,刘经南,赵建虎.多波束测深数据的异常检测和滤波[J].武汉大学学报.信息科学版,2004,29(1):80-83.
[117]阳凡林,刘经南,赵建虎.多波束测深数据的异常检测和滤波[J].武汉大学学报.信息科学版,2004,29(1):80-83.
[118]阳凡林,赵建虎,周丰年,独知行.多波束声纳测深瞬间到达角和旅行时的精确确定[J].武汉大学学报.信息科学版,2006,31(3):218-220.
[119]阳凡林,吴自银,独知行,金翔龙.多波束声纳和侧扫声纳数字信息的配准及融合[J].武汉大学学报.信息科学版,2006,31(8):740-743.
[120]姚恩涛,张君,季娟.基于EMD-RBF网络的车辆动态称重信号处理方法[J].传感器与微系统,2007,26(1):80-83.
[121]姚恩涛,季娟,张明,两轴车辆动态称重信号分析方法研究[J].传感器技术,2005,24(12):22-25.
[122]业宁,孙瑞祥,董逸生.多拉格朗日乘子协同优化的SVM快速学习算法研究[J].计算机研究与发展, 2006,43(3):442-448.
[123]尹逊福,徐龙,熊学军,刘爱菊.南海东部区域的海流状况——Ⅲ.海流的经验模分解和Hibert谱[J].海洋科学进展,2003,21(2):148-159.
[124]于家城,陈家斌,徐学强,宋春雷.多波束系统数字测深模型设计[J].海洋测绘,2007,27(6):33-35.
[125]于哲峰,杨智春.经验模分解中的间歇性控制方法改进及分解精度控制[J].机械科学与技术,2007,26(5):635-642.
[126]曾嵘,蒋新华,刘建成.基于支持向量机的异常值检测的两种方法[J].信息技术,2004,28(5):3-4.
[127]曾毅,刘成才.遗传算法在边坡稳定分析中的应用[J].中州大学学报,2007,24(3):123-126.
[128]张朝元,胡光华,徐天泽.基于LS-SVM的交通流量时间序列预测[J].云南大学学报(自然科学版),2004,26(B07):19-22.
[129]张浩然,汪晓东.回归最小二乘支持向量机的增量和在线式学习算法[J].计算机学报, 2006,29(3): 400-406.
[130]张浩然,汪晓东,张长江.一种鲁棒回归支持向量机及其学习算法[J].南京理工大学学报(自然科学版),2006,30(3):311-314.
[131]张君,姚恩涛,倪国芬,季娟.图像分类结合经验模分解和径向基函数网络在车辆动态称重中的应用[J].机械科学与技术,2007,26(6):701-704.
[132]张伟,胡昌华,焦李成,尚荣华.遗忘因子最小二乘支持向量机及在陀螺仪漂移预测中的应用研究[J].宇航学报,2007,28(2):448-451.
[133]张小平.高分辨率多波束成像声呐关键技术研究[D].哈尔滨工程大学,2005.
[134]张旭,张永刚,张胜军,吴世华.经验正交函数与广义数值环境模式重构声速剖面的比较[J].应用声学,2010,29(2):115-121.
[135]张异彪.多波束勘测的特殊地形处理[J].海洋测绘,2004,24(3):16-19.
[136]翟春平.基于电子海图的浅水多波束测量导航方法研究与软件开发[D].哈尔滨工程大学,2006.
[137]赵洪伟,杨胜雄,张志荣,彭朝旭,关永贤.多波束测深系统的高精度差分GPS数据实时优化[J].中国区域地质,2001,20(4):411-414.
[138]赵会滨,徐新盛,吴英姿.多波束条带测深技术发展动态展望[J].哈尔滨工程大学学报,2001,22(2):41-45.
[139]赵建虎,周丰年,张红梅,王胜平.局域空间声速模型的建立方法研究[J].武汉大学学报.信息科学版,2008,33(2):199-202.
[140]赵建虎,刘经南.精密多波束测深系统位置修正方法研究[J].武汉大学学报.信息科学版,2002,5(5):473-477.
[141]赵建虎.多波束深度及图像数据处理方法研究[D].武汉大学,2002,70-72.
[142]赵建虎,张红梅,John E Hughes Clarke,王胜平.多波束测量中换能器瞬时精密高程的确定[J].武汉大学学报.信息科学版,2006,31(11):983-986.
[143]赵建虎,刘经南,阳凡林.多波束测深数据系统误差的削弱方法研究[J].武汉大学学报.信息科学版,2004,29(5):394-397.
[144]赵建虎,刘经南.多波束测深系统的归位问题研究[J].海洋测绘,2003,23(1):6-8.
[145]周辉仁,郑丕谔,赵春秀.基于遗传算法的LS-SVM参数优选及其在经济预测中的应用[J].计算机应用,2007,27(6):1418-1420.
[146]周士弘,张茂有,周曰鹏.海洋声速场的经验正交函数描述及声速剖面预报[J].海洋通报,1999,18(5):27-34.
[147]周天,李海森,朱志德,袁延艺.多波束测深系统多子阵检测法的改进及其性能分析[J].声学技术,2005,24(3):152-156.
[148]周天.超宽覆盖海底地形地貌高分辨探测技术研究[D].哈尔滨工程大学,2005.
[149]朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002,28(5):555-559.
[150]朱庆,李德仁.多波束测深数据的误差分析与处理[J].武汉大学学报.信息科学版,1998,23(1):43-46.
[151]朱小辰,肖付民,刘雁春,葛健.表层声速对多波束测深影响的研究[J].海洋测绘,2007,27(3):23-29.
[152]朱旭初.便携式多波束测深仪实时波束形成技术研究与实现[D].哈尔滨工程大学,2004.
[153]邹炎飚,林兆花,谢存禧.监护信息系统中异常值的识别和处理方法[J].测试技术学报,2007,21(6):531-535.
[154] B Sholkopf, A Smola, K R Muller. Nonlinear component analysis as a kernel eigenvalue problem [J] Neural Comput, 1998.
[155] B Sholkopf, K Sung, C J C Burges, et al. Comparing support vector machine with Gaussian kernels to radial basis function classifiers [J] IEEE Trans. Signal Processing, 1997.
[156] B Scholkopf, S Mika, CJC Burges, et al. Input space versus feature space in kernel-based methods [J] IEEE. Trans. Neural Networks, 1999.
[157] Bisquay H,Freulon X,Fouquet C,et al.Multibeam Data Cleaning for Hydrography Using Geostatistics.IEEE,1998.
[158] Barber C B,DObkin D P,Huhdanpaa H T.The Quickhull Algorithm for Convex Hulls [J]. ACM Transaction on Mathematical Software,1996,22(4).
[159] Beyer A ,Klenke H W S,Niederjasper F.High resolution bathymetry of the eastern slope of the Porcupine Seabight[J].Marine Geology,2003,198:27-54.
[160] Bisquay H,Freulon X,De Fouquet C,et al.Multibeam Data Cleaning for Hydrography Using Geostatistics[C].OCEAN’98 Conference Proceedings.Nice,France:1998:1135-1143.
[161] Bottelier P, Haagmans R,Kinneging N.Fast Reduction of High Density Multibeam Echosounder Data for Near Real-Time Applications[J].Hydrographic Journal,2000,(98):23-28.
[162] B.R.Calder,L.A.Mayer.Automatic Processing of High-Rate,High-Density Multibeam echosounder data[J].An Electronic Journal of The Earth Sciences,2003,4(6):1-22.
[163] Canepa G, Bergern O, Pace N G.A New Algorithm for Automatic Processing of Bathymetric Data [J].IEEE Journal of Oceanic Engineering, 2003, 28(1):62-77.
[164] Caress D W, Chayes D N.Improved Processing of Hydrosweep DS Multibeam Data on the R/V Maurice Ewing [J].Marine Geophysical Researches, 1996, 18(6):631-650.
[165] CARIS.HIPS and SIPS 6.1.1 User’s Guide [M].Fredericton NB: Universal System Ltdp., 2007.
[166] Calder B, Mayer LA.Robust Automatic Multibeam Bathymetric Processing[C]. U.S.Hydrographic Conference.Norfolk, VA, USA: 2001.
[167] Calder B,Smith S.A Time Comparison of Computer-Assisted and Manual Bathymetric Processing[J].International Hydrographic Review,2004,5(1):10-23.
[168] C. J. Burges,“A tutorial on support vector machines for pattern recognition,”Data Mining and Knowledge Discovery, 1998,2 (2):121-167.
[169] Cortes C,Vapnik V .Support—Vector Network[J].Machine Learning,1995,20(3):273-297.
[170] Chiang J H,Hao P Y.Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling:A New Approach[J].IEEE Trans on Fuzzy Systems,2004,12(1):1-11.
[171] DAVID W C,DALEN C.Multibeam dataprocessing of hydrosweep DS[J].Marine Geophysical Researches,1996,18:631-650.
[172] Debese, N., Use of a robust estimator for automatic detection of isolated errors appearing in bathymetry data [J]. Int. Hydrogr. Rev., 2001, 2: 32–44.
[173] Depner J,Reed B,Byrne S,et al.Dealing with Increasing Data Volumes and Decreasing Resource[C].OCEANS 02 MTS/IEEE. Biloxi, Mississippi: 2002:1212-1222.
[174] De Wulf A,Hennau M,Constales D.TIN-Based Digital Terrain Modeling Using Multibeam Data[C].Proceedings of the 15th International Congress of the International Federation of Hydrographic Societies.Provincial House Antwerp,Belgium:2006b:186-189.
[175] Du Z, Wells D E, Mayer L A.An approach to automatic detection of outliers in multibeam echo sounding data [J].Hydrographic Journal, 1996, 79:19-25.
[176] Dyn N, Levin D, Rippa S.Data dependent triangulation for piecewise linear interpolation [J].IMA Journal of Numerical Analysis, 1990, 10(1):137-154.
[177] Eeg, J. On the identification of spikes in soundings [J]. Int. Hydrogr.Rev., 1995, 72: 33–41.
[178] F Girosi, M Jones, T Poggio. Regularization theory and neural networks architectures [J] Neural Comput, 1995.
[179] Guenther G C, Green J E.Improved depth selection in the bathymetric swath survey system (BS3) combined offline processing (COP) algorithm[R].Rockville, MD: National Oceanic and Atmospheric Administration, Dept.of Commerce, 1982.
[180] HUANG Xianyuan, ZHAI Guojun, SUI Lifen, CHAI Hongzhou. Study on the detection of abnormal sounding data based on LS-SVM [J].Acta Oceanologica Sinica, 2010, 29(6):115-120.
[181] IHO Committee.Manual on Hydrography [M].Monaco: International Hydrographic Bureau, 2005.
[182] IHO Standards for Hydrographic Surveys, Special Publication No.44[S].4th.International Hydrographic Bureau, 1996.
[183] IHO Standards for Hydrographic Surveys, Special Publication No.44[S].5th.International Hydrographic Bureau, 2008.
[184] Jakobsson M, Calder B, Mayer L.On the effect of random errors in gridded bathymetric compilations [J].Journal of geophysical research, 2002, 107(B12):14.1-14.11.
[185] Kammerer E,Charlot D,Guillaudeux S,et al.Comparative Study of Shallow Water Multibeam Image for Cleaning Bathymetry Sounding Errors[C].OCEANS 2001 MTS/IEEE Conference and Exhibition.Honolulu,HI,USA:2001:2124-2128.
[186] K.-M. Chung, W.-C. Kao, T. Sun, L.-L. Wang and C.-J. Lin,“Radiusmargin bounds for support vector machines with the RBF kernel,”NeuralComputation, 2003 ,15(11):2643-2681.
[187] Legleiter C J, Kyriakidis P C.Spatial prediction of river channel topography by kriging [J].Earth Surface Processes and Landforms, 2008, 33(6):841-867.
[188] Lirakis C B,KPB.Automated Multibeam Data Cleaning and Target Detection[C].OCEANS2000 MTS/IEEE Conference and Exhibition.Providence,RI,USA:2000:719-723.
[189] Ma J S,James Theiler,Simon Perkins.Accurate On-line Support Vector Regression [J].Neural Computation,2003,15(11):2683-2703.
[190] Mallace D, Robertson P.Alternative Use of CUBE: How to Fit a Square Peg in a Round Hole[C].U.S.Hydrographic Conference.Norfolk, Virginia: 2007.
[191] Massimiliano Pontil, Alessandro Verri. Properties of support vector machines [J] Neural Comput, 1998.
[192] N. Cristianini and J. Shawe-Taylor, An introduction to Support VectorMachines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press, 2000.
[193] Suykens JAK,De Brabanter J,Lukas L,et a1.Weighted Least Squares Support Vector Machines:Robustness and Sparse Approximation [J].Neurocomputing,2002,48(1-4):85-105.
[194] Suykens JAK, Van Gestel T, Baestaens D,et a1. Least square support vector machines[M].World Scientific,Singapore,2002,265.
[195] Suykens JAK, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Process Letters, 1999, 9(3):293-300
[196] Suykens JAK. Least Squares Support Vector Machines for Classification and Nonlinear Modeling [J].Neural Network World, 2000, 10(1):29-48.
[197] S Amari, S Wu. Improving support vector machine classifier by modifying kernel function [J] Neural Networks, 1999.
[198] Van Gestel T,Suykens JAK,Baestaens D,et a1.Financial Time Series Prediction Using Least Squares Support Vector Machines within the Evidence Framework[J].IEEE Trans on Neural Networks,2001,12(4):809-821.
[199] Vapnik V.Statistical Learning Theory [M].New York: John Wiley, 1998.399.
[200] Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer,1995.
[201] Vapnik V.S Golowich, A J Smola. Support vector method for function approximation, regression, and signal processing [C] in: NIPS 9, San, 1997.
[2]曹玲,窦永祥.黑河流域降水的时空特征及预报方法[J].干旱气象,2005,23(2):35-38.
[3]陈将宏,周德强,李落清.加权最小二乘支持向量机与最小二乘问题的关系[J].湖北大学学报.自然科学版,2004,26(1):16-18.
[4]陈磊,张土乔.基于最小二乘支持向量机的时用水量预测模型[J].哈尔滨工业大学学报,2006,38(9):1528-1530.
[5]陈上及,马继瑞.海洋数据处理分析方法及其应用[M].北京:海洋出版社,1991.
[6]陈伟,胡昌华,曹小平等.基于最小二乘支撑矢量机的陀螺仪漂移预测[J].宇航学报,2006,27(1):135-138.
[7]陈效逑,彭嘉栋,李慧敏.内蒙古地区气温变化的季节和区域差异[J].地理研究,2009,28(1):27-35.
[8]陈友元.便携式多波束测深仪实时测控与数据可视技术研究[D].哈尔滨工程大学,2005.
[9]陈隽,徐幼麟,李杰. Hilbert—Huang变换在密频结构阻尼识别中的应用[J].地震工程与工程振动,2003,23(4):34-42.
[10]陈隽,徐幼麟,李杰.经验模分解及小波分析在结构损伤识别中的应用:试验研究[J].地震工程与工程振动,2007,27(1):110-116.
[11]丁继胜,周兴华,吴永亭,胡光海.多波束回声测深系统测量数据的分离提取方法[J].海洋测绘,2006,26(4):33-35.
[12]丁继胜,周兴华,唐秋华,陈义兰.多波束勘测声速剖面场的EOF表示方法[J].武汉大学学报.信息科学版,2007,32(5):446-449.
[13]董江,任立生.基于趋势面的多波束测深数据滤波方法[J].海洋测绘,2007,27(6):25-28
[14]董庆亮,韩红旗,方兆宝,潘乐等.声速剖面改正对多波束测深的影响[J].海洋测绘,2007,27(2):56-58.
[15]董西国.支持向量机在数据挖掘中的应用[D].哈尔滨工程大学, 2002.
[16]董云杰.支持向量机及其在模式识别中应用的研究[D].兰州理工大学, 2003.
[17]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2006.
[18]邓兴升,花向红.动态最小二乘支持向量机学习算法[J].武汉大学学报.信息科学版,2008,33(11):1122-1125.
[19]杜树新,吴铁军.模式识别中的支持向量机方法[J].浙江大学学报(工学版),2003,37(5):521-527.
[20]范震寰.多波束测深关键技术研究[D].南京航空航天大学,2005.
[21]封筠,陈志军,李莉蓉.基于修正核函数的SVM分类器研究[J].系统仿真学报, 2006,18(3):570-572.
[22]封筠,颉斌,郝卫东,杨扬.一种SVM分类器自动模型选择方法[J].北京科技大学学报,2006,28(1):88-92.
[23]傅佩红,李雪飞,贺立源.遗传算法在特异值识别与处理中的应用[J].测绘科学,2008,33(2):192-194.
[24]高金耀,金翔龙,吴自银.多波束数据的海底数字地形模型构建[J].海洋通报,2003,22(1):30-38.
[25]高金耀,方银霞,徐赛英.原始多波束数据的格式转换与统一[J].海洋通报,2002,21(6):68-74.
[26]郭发滨,周兴华,陈义兰.多波束测深数据出现失真的因果分析[J].海洋测绘,2008,28(1):59-61.
[27]郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究[J].系统仿真学报,2006,18(7):2033-2035.
[28]郭辉,王玲,刘贺平.基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题[J].北京科技大学学报,2006,28(3): 303-306.
[29]郭辉,刘贺平,王玲.基于最小二乘支持向量机对偶优化问题的核偏最小二乘[J].北京科技大学学报,2006,28(8):790-793.
[30]韩梅,魏亮,周艳霞.改进型经验正交函数海洋声速剖面预报方法[J].海洋科学,2009,33(1):30-33.
[31]何高文.多波束测深系统声速校正[J].海洋技术,2000,19(4):14-21.
[32]何义斌,吴书帮,谢洪燕,赵建虎.多波束异常测深数据检测方法实践[J].测绘科学,2004,29(1):50-52.
[33]侯世喜,黄谟涛,欧阳永忠,陆秀平等.多波束数据处理软件的应用与启示[J].海洋测绘,2003,23(6):14-17.
[34]胡国胜,钱玲,张国红.支持向量机的多分类算法[J].系统工程与电子技术,2006,28(1):127-132.
[35]胡清华,张建华,李海森,魏玉阔.利用MB-system软件进行多波束测深数据处理的研究[J].海洋测绘,2006,26(5):28-30.
[36]黄谟涛,翟国君,欧阳永忠,刘雁春,陆秀平,王克平.多波束与单波束测深数据的融合处理技术[J].测绘学报,2001,30(4):299-303.
[37]黄谟涛,翟国君,管铮等.多波束测深技术研究进展与展望[A].第十二届海洋测绘综合性学术研讨会,2001:3-8.
[38]黄贤源,翟国君,隋立芬,黄谟涛,欧阳永忠,柴洪洲. LS-SVM算法中优化训练样本对测深异常值剔除的影响[J].测绘学报,2011,40(1):22-27.
[39]黄贤源,隋立芬,翟国君,柴洪洲,王海栋.基于Bayes估计的多波束测深异常数据探测方法[J].武汉大学学报.信息科学版,2010,35(2):168-171.
[40]黄贤源,翟国君,隋立芬,黄谟涛等.最小二乘支持向量机在海洋测深异常值探测中的应用[J].武汉大学学报.信息科学版,2010,35(10):1188-1191.
[41]黄贤源,翟国君,黄谟涛,隋立芬,柴洪洲,陆秀平.利用水深不确定度探测测深异常值的方法[J].测绘科学技术学报,2011,28(1):70-74.
[42]黄贤源,隋立芬,翟国君,柴洪洲等.遗传算法在海洋测深异常数据处理中的应用[J].测绘科学技术学报,2010,27(4):247-250.
[43]黄贤源,翟国君,黄谟涛,隋立芬.最小二乘支持向量机构造海底趋势面的论证研究[J].海洋测绘,2010,30(3):9-12.
[44]黄贤源,隋立芬,王威,牟忠凯.有色噪声条件下野值的辨识与处理[J].辽宁工程技术大学学报.信息科学版,2010,29(2):212-215.
[45]季娟,姚恩涛,张明,刘兼唐.基于HHT的汽车动态称重数据分析方法[J].数据采集与处理,2006,21(3):340-344.
[46]江力,李长云.基于经验模分解的小波阈值滤波方法研究[J].信号处理,2005,21(6):659-662.
[47]孔锐,张冰.一种快速最小二乘支持向量机分类算法[J].计算机工程与应用,2007,43(32):167-170.
[48]兰光华.支持向量机训练算法实现及其改进[D].南京理工大学,2005.
[49]赖仲淄,陈坚雄,郑道贤,潘剑中,吴宏旭.SEABEAM1185多波束系统的测量精度分析及在堤防监测中的应用[J].海洋测绘,2007,27(6):58-61.
[50]李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报(工学版),2006,40(2):202-206.
[51]李海森,黎子盛,周天,孙圣和.MSB算法在多波束测深系统中的应用[J].声学技术,2007,26(2):286-290.
[52]李家彪,王小波,华祖根等.多波束勘查原理技术与方法[M].北京:海洋出版社,1999 ,197-207.
[53]李家彪,郑玉龙,王小波,吴自银.多波束测深及影响精度的主要因素[J].海洋测绘,2001,(1):26-32.
[54]李凯,郭子雪.基于SVM的函数模拟[J].河北大学学报(自然科学版),2001,21(1):7-8.
[55]李莉丽.浅水多波束条带测深仪实时显控系统技术的研究[D].哈尔滨工程大学,2005.
[56]李琳,张晓龙.支持向量机学习方法的选择与应用[J].武汉科技大学学报(自然科学版), 2006,29(1):75-78.
[57]李天云,王爱凤,程思勇,吴正非.用LS-SVM分析油浸式变压器故障[J].高电压技术,2008,34(2):346-349.
[58]李文杰,胡平,肖都,刘春雷.多波束测深在海洋工程勘察中的应用[J].物探与化探,2004,28(4):373-376.
[59]李新娜,归庆明,许阿裴.基于识别变量的粗差探测Bayes方法[J],测绘学报,2008,37(3):355-360.
[60]李新娜.多个粗差探测的Bayes方法与应用[D].解放军信息工程大学,2008:15-25.
[61]李宜龙,李岳和,许军.水位观测数据粗差探测技术研究[M],中国测绘学会海洋测绘专业委员会第二十届海洋测绘综合性学术研讨会论文集,2008,:39-40.
[62]李治友.遗传算法和支持向量机混合方法及其应用[D].重庆大学,2003.
[63]刘汉军.多波束全景扫描声呐设计与实现[D].中国科学院声学研究所,2002.
[64]刘胜旋,关永贤.多波束系统的参数误差判断及校正[J].海洋测绘,2002,22(1):33-37.
[65]刘方兰,张志荣,余平.多波束系统横摇、纵倾参数的校正方法[J].吉林大学学报(地球科学版),2004,34(4):621-624.
[66]刘小茂,孔波,高俊斌,张钧.一种稀疏最小二乘支持向量分类机[J].模式识别与人工智能,2007,20(5):681-687.
[67]刘雁春,肖付民,暴景阳,徐卫明.海道测量学概论[M].北京,测绘出版社,2006.
[68]刘雁春.海洋测深空间结构及其数据处理[D].武汉:武汉测绘科技大学,1998.
[69]刘志刚,秦前清,李德仁,王新洲.基于混合核函数的支撑向量机及其在遥感影像土地利用分类中的应用[J].测绘信息与工程, 2003,28(5):1-3.
[70]鹿卫国,戴亚平,涂序彦,高峰.适用于加权样本集处理的加权支持向量机方法[J].北京理工大学学报,2005,25(3):211-215.
[71]陆秀平,黄谟涛,翟国君,欧阳永忠,徐广袖,王克平.多波束测深数据处理及管理系统设计与开发[J].海洋测绘,2006,26(6):1-5.
[72]骆剑承,周成虎,梁怡,马江洪.支撑向量机及其遥感影像空间特征提取和分类的应用研究[J].遥感学报,2002,6(1):50-55.
[73]罗深荣.侧扫声纳和多波束测深系统在海洋调查中的综合应用[J].海洋测绘,2003,23(1):22-24.
[74]吕海龙,杜德文,刘炎光,杨刚,田丽艳,闫仕娟.多波束回声数据的统计与底质分类应用[J].海洋科学进展,2006,24(4):463-471.
[75]吕剑峰,戴连奎.加权最小二乘支持向量机改进算法及其在光谱定量分析中的应用[J].分析化学,2007,35(3):340-344.
[76]马开银.小型化多波束条带测深系统研究[D].哈尔滨工程大学,2002.
[77]马建林,金菁,刘勤,来向华.多波束与侧扫声纳海底目标探测的比较分析[J].海洋测绘,2006,26(3):10-12.
[78]马建林,金菁,来向华.多波束测深海底数字地形模型的建立[J].海洋测绘,2005,25(5):15-17.
[79]马文亮,卢秋芽.多波束在海底复杂地形条件下大比例尺测量方法探讨[J].上海地质,2006,(1):19-22.
[80]梅建新,段汕,秦前清.基于支持向量机的特定目标检测方法[J].武汉大学学报.信息科学版,2004,29(10):912-915.
[81]聂良春,朱琦.多波束测深相位检测法中一种有效的相位差序列提取方法[J].舰船科学技术,2003,25(6):3-5.
[82]聂良春,朱琦,李海森.幅度-相位联合检测法在多波束测深系统中的应用[J].声学技术,2005,24(2):84-88.
[83]聂良春.多波束相位差法海底TOA估计理论分析及实用研究[D].哈尔滨工程大学,2003.
[84]乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方法[J].电子学报, 2006,34(3):496-498.
[85]阮锐,邵海涛.多波束测深系统内部参数的检测与分析[J].海洋测绘,2001,(4):51-54.
[86]沈斌,姚敏,易文晟.基于最小二乘支持向量机的模糊时序分析方法[J].浙江大学学报(工学版),2005,39(8):1142-1146.
[87]舒忠平,杨智春.抑制经验模分解边缘效应的极值点对称延拓法[J].西北工业大学学报,2006,24(5):639-643.
[88]宋平舰,张杰.二维经验模分解在海洋遥感图像信息分离中的应用[J].高技术通讯,2001,(9):62-67.
[89]宋志宇,李俊杰.最小二乘支持向量机在大坝变形预测中的应用[J].水电能源科学,2006,24(6):49-52.
[90]孙海,陈伟,陈隽.强风环境非平稳风速模型及应用[J].防灾减灾工程学报,2006,26(1):52-57.
[91]孙岚,刘雁春,陆秀平,欧阳永忠,李明叁,许家琨.利用CUBE算法处理多波束测深数据研究[A].第二十一届海洋测绘综合性学术研讨会,2009:122-126.
[92]谭琨,杜培军,郑辉.支持向量机在空间信息处理领域的应用研究[J].测绘科学,2007,32(2):87-89.
[93]谭小彬,奚宏生,王卫平,殷保群.基于支持向量机的异常检测[J].中国科学技术大学学报,2003,33(5):599-605.
[94]王冬梅,吴杰.基于多波束测深系统的三维河势分析[J].水利水电技术,2004,35(2):92-93.
[95]王国胜,钟义信.支持向量机的若干新进展[J].电子学报,2001,29(10):1397-1400.
[96]王海栋.多波束系统测深异常处理理论与方法研究[D].中国人民解放军信息工程大学,2010.
[97]王红军,徐小力.支持向量机在设备故障诊断方面的应用研究概述[J].机械设计与制造,2005,(9):157-159.
[98]王琪,刘雁春,暴景阳.关于多波束与侧扫声纳回波检测方法的探讨[J].海洋技术,2003,22(3):70-73.
[99]王闰成,卫国兵.多波束探测技术的应用[J].海洋测绘,2003,23(5):20-23.
[100]王伟,魏洪兴,陈殿生,王田苗.基于经验模分解和最小二乘支持矢量机的装载机载质量动态测量混合建模方法[J].机械工程学报,2008,44(2):87-93.
[101]王向红.多波束条带测深系统数据后置处理技术研究[D].哈尔滨工程大学,2005.
[102]王新晓,黄建国,褚福照,张永峰.数字多波束目标被动定向仿真技术研究[J].应用声学,2004,23(6):31-34.
[103]魏凤英.现代气候统计诊断与预测技术[M].北京:气象出版社,2007.
[104]魏凤英,冯蕾,马玉平,王石立.东北地区玉米气候生产潜力时空分布特征[J].气象科技,2010,38(2):243-247.
[105]吴少琴.多波束条带测深仪拼图技术的研究[D].哈尔滨工程大学,2003.
[106]吴英姿,徐新盛,乔力争.多波束测深系统的精度评估方法研究[J].海洋技术,2003,22(3):65-69.
[107]吴英姿.多波束测深系统地形跟踪与数据处理技术研究[D].哈尔滨工程大学,2001.
[108]吴自银,李家彪.多波束勘测的数据编辑方法[J].海洋通报,2000,19(3):74-78.
[109]吴自银,金翔龙,郑玉龙,李家彪,余平.多波束测深边缘波束误差的综合校正[J].海洋学报,2005,27(4):88-94.
[110]吴兆福,宫鹏,高飞,王侬.基于支持向量机的GPS似大地水准面拟合[J].测绘学报,2004,33(4):303-306.
[111]肖付民,暴景阳,吕仁臣.多波束坐标系统及误差源分析[J].海洋测绘,2001,(2):41-44.
[112]熊学军,郭炳火,胡筱敏,刘建军.EMD方法和Hilbert谱分析法的应用与探讨[J].黄渤海海洋,2002,20(2):12-21.
[113]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策,2004,19(5):481-484.
[114]许新发,樊宜.SeaBat8101多波束测深系统及其应用[J].江西水利科技,2001,27(4):211-214.
[115]阎辉,张学工,李衍达.支持向量机与最小二乘法的关系研究[J].清华大学学报.自然科学版,2001,41(9):77-80.
[116]阳凡林,刘经南,赵建虎.多波束测深数据的异常检测和滤波[J].武汉大学学报.信息科学版,2004,29(1):80-83.
[117]阳凡林,刘经南,赵建虎.多波束测深数据的异常检测和滤波[J].武汉大学学报.信息科学版,2004,29(1):80-83.
[118]阳凡林,赵建虎,周丰年,独知行.多波束声纳测深瞬间到达角和旅行时的精确确定[J].武汉大学学报.信息科学版,2006,31(3):218-220.
[119]阳凡林,吴自银,独知行,金翔龙.多波束声纳和侧扫声纳数字信息的配准及融合[J].武汉大学学报.信息科学版,2006,31(8):740-743.
[120]姚恩涛,张君,季娟.基于EMD-RBF网络的车辆动态称重信号处理方法[J].传感器与微系统,2007,26(1):80-83.
[121]姚恩涛,季娟,张明,两轴车辆动态称重信号分析方法研究[J].传感器技术,2005,24(12):22-25.
[122]业宁,孙瑞祥,董逸生.多拉格朗日乘子协同优化的SVM快速学习算法研究[J].计算机研究与发展, 2006,43(3):442-448.
[123]尹逊福,徐龙,熊学军,刘爱菊.南海东部区域的海流状况——Ⅲ.海流的经验模分解和Hibert谱[J].海洋科学进展,2003,21(2):148-159.
[124]于家城,陈家斌,徐学强,宋春雷.多波束系统数字测深模型设计[J].海洋测绘,2007,27(6):33-35.
[125]于哲峰,杨智春.经验模分解中的间歇性控制方法改进及分解精度控制[J].机械科学与技术,2007,26(5):635-642.
[126]曾嵘,蒋新华,刘建成.基于支持向量机的异常值检测的两种方法[J].信息技术,2004,28(5):3-4.
[127]曾毅,刘成才.遗传算法在边坡稳定分析中的应用[J].中州大学学报,2007,24(3):123-126.
[128]张朝元,胡光华,徐天泽.基于LS-SVM的交通流量时间序列预测[J].云南大学学报(自然科学版),2004,26(B07):19-22.
[129]张浩然,汪晓东.回归最小二乘支持向量机的增量和在线式学习算法[J].计算机学报, 2006,29(3): 400-406.
[130]张浩然,汪晓东,张长江.一种鲁棒回归支持向量机及其学习算法[J].南京理工大学学报(自然科学版),2006,30(3):311-314.
[131]张君,姚恩涛,倪国芬,季娟.图像分类结合经验模分解和径向基函数网络在车辆动态称重中的应用[J].机械科学与技术,2007,26(6):701-704.
[132]张伟,胡昌华,焦李成,尚荣华.遗忘因子最小二乘支持向量机及在陀螺仪漂移预测中的应用研究[J].宇航学报,2007,28(2):448-451.
[133]张小平.高分辨率多波束成像声呐关键技术研究[D].哈尔滨工程大学,2005.
[134]张旭,张永刚,张胜军,吴世华.经验正交函数与广义数值环境模式重构声速剖面的比较[J].应用声学,2010,29(2):115-121.
[135]张异彪.多波束勘测的特殊地形处理[J].海洋测绘,2004,24(3):16-19.
[136]翟春平.基于电子海图的浅水多波束测量导航方法研究与软件开发[D].哈尔滨工程大学,2006.
[137]赵洪伟,杨胜雄,张志荣,彭朝旭,关永贤.多波束测深系统的高精度差分GPS数据实时优化[J].中国区域地质,2001,20(4):411-414.
[138]赵会滨,徐新盛,吴英姿.多波束条带测深技术发展动态展望[J].哈尔滨工程大学学报,2001,22(2):41-45.
[139]赵建虎,周丰年,张红梅,王胜平.局域空间声速模型的建立方法研究[J].武汉大学学报.信息科学版,2008,33(2):199-202.
[140]赵建虎,刘经南.精密多波束测深系统位置修正方法研究[J].武汉大学学报.信息科学版,2002,5(5):473-477.
[141]赵建虎.多波束深度及图像数据处理方法研究[D].武汉大学,2002,70-72.
[142]赵建虎,张红梅,John E Hughes Clarke,王胜平.多波束测量中换能器瞬时精密高程的确定[J].武汉大学学报.信息科学版,2006,31(11):983-986.
[143]赵建虎,刘经南,阳凡林.多波束测深数据系统误差的削弱方法研究[J].武汉大学学报.信息科学版,2004,29(5):394-397.
[144]赵建虎,刘经南.多波束测深系统的归位问题研究[J].海洋测绘,2003,23(1):6-8.
[145]周辉仁,郑丕谔,赵春秀.基于遗传算法的LS-SVM参数优选及其在经济预测中的应用[J].计算机应用,2007,27(6):1418-1420.
[146]周士弘,张茂有,周曰鹏.海洋声速场的经验正交函数描述及声速剖面预报[J].海洋通报,1999,18(5):27-34.
[147]周天,李海森,朱志德,袁延艺.多波束测深系统多子阵检测法的改进及其性能分析[J].声学技术,2005,24(3):152-156.
[148]周天.超宽覆盖海底地形地貌高分辨探测技术研究[D].哈尔滨工程大学,2005.
[149]朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002,28(5):555-559.
[150]朱庆,李德仁.多波束测深数据的误差分析与处理[J].武汉大学学报.信息科学版,1998,23(1):43-46.
[151]朱小辰,肖付民,刘雁春,葛健.表层声速对多波束测深影响的研究[J].海洋测绘,2007,27(3):23-29.
[152]朱旭初.便携式多波束测深仪实时波束形成技术研究与实现[D].哈尔滨工程大学,2004.
[153]邹炎飚,林兆花,谢存禧.监护信息系统中异常值的识别和处理方法[J].测试技术学报,2007,21(6):531-535.
[154] B Sholkopf, A Smola, K R Muller. Nonlinear component analysis as a kernel eigenvalue problem [J] Neural Comput, 1998.
[155] B Sholkopf, K Sung, C J C Burges, et al. Comparing support vector machine with Gaussian kernels to radial basis function classifiers [J] IEEE Trans. Signal Processing, 1997.
[156] B Scholkopf, S Mika, CJC Burges, et al. Input space versus feature space in kernel-based methods [J] IEEE. Trans. Neural Networks, 1999.
[157] Bisquay H,Freulon X,Fouquet C,et al.Multibeam Data Cleaning for Hydrography Using Geostatistics.IEEE,1998.
[158] Barber C B,DObkin D P,Huhdanpaa H T.The Quickhull Algorithm for Convex Hulls [J]. ACM Transaction on Mathematical Software,1996,22(4).
[159] Beyer A ,Klenke H W S,Niederjasper F.High resolution bathymetry of the eastern slope of the Porcupine Seabight[J].Marine Geology,2003,198:27-54.
[160] Bisquay H,Freulon X,De Fouquet C,et al.Multibeam Data Cleaning for Hydrography Using Geostatistics[C].OCEAN’98 Conference Proceedings.Nice,France:1998:1135-1143.
[161] Bottelier P, Haagmans R,Kinneging N.Fast Reduction of High Density Multibeam Echosounder Data for Near Real-Time Applications[J].Hydrographic Journal,2000,(98):23-28.
[162] B.R.Calder,L.A.Mayer.Automatic Processing of High-Rate,High-Density Multibeam echosounder data[J].An Electronic Journal of The Earth Sciences,2003,4(6):1-22.
[163] Canepa G, Bergern O, Pace N G.A New Algorithm for Automatic Processing of Bathymetric Data [J].IEEE Journal of Oceanic Engineering, 2003, 28(1):62-77.
[164] Caress D W, Chayes D N.Improved Processing of Hydrosweep DS Multibeam Data on the R/V Maurice Ewing [J].Marine Geophysical Researches, 1996, 18(6):631-650.
[165] CARIS.HIPS and SIPS 6.1.1 User’s Guide [M].Fredericton NB: Universal System Ltdp., 2007.
[166] Calder B, Mayer LA.Robust Automatic Multibeam Bathymetric Processing[C]. U.S.Hydrographic Conference.Norfolk, VA, USA: 2001.
[167] Calder B,Smith S.A Time Comparison of Computer-Assisted and Manual Bathymetric Processing[J].International Hydrographic Review,2004,5(1):10-23.
[168] C. J. Burges,“A tutorial on support vector machines for pattern recognition,”Data Mining and Knowledge Discovery, 1998,2 (2):121-167.
[169] Cortes C,Vapnik V .Support—Vector Network[J].Machine Learning,1995,20(3):273-297.
[170] Chiang J H,Hao P Y.Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling:A New Approach[J].IEEE Trans on Fuzzy Systems,2004,12(1):1-11.
[171] DAVID W C,DALEN C.Multibeam dataprocessing of hydrosweep DS[J].Marine Geophysical Researches,1996,18:631-650.
[172] Debese, N., Use of a robust estimator for automatic detection of isolated errors appearing in bathymetry data [J]. Int. Hydrogr. Rev., 2001, 2: 32–44.
[173] Depner J,Reed B,Byrne S,et al.Dealing with Increasing Data Volumes and Decreasing Resource[C].OCEANS 02 MTS/IEEE. Biloxi, Mississippi: 2002:1212-1222.
[174] De Wulf A,Hennau M,Constales D.TIN-Based Digital Terrain Modeling Using Multibeam Data[C].Proceedings of the 15th International Congress of the International Federation of Hydrographic Societies.Provincial House Antwerp,Belgium:2006b:186-189.
[175] Du Z, Wells D E, Mayer L A.An approach to automatic detection of outliers in multibeam echo sounding data [J].Hydrographic Journal, 1996, 79:19-25.
[176] Dyn N, Levin D, Rippa S.Data dependent triangulation for piecewise linear interpolation [J].IMA Journal of Numerical Analysis, 1990, 10(1):137-154.
[177] Eeg, J. On the identification of spikes in soundings [J]. Int. Hydrogr.Rev., 1995, 72: 33–41.
[178] F Girosi, M Jones, T Poggio. Regularization theory and neural networks architectures [J] Neural Comput, 1995.
[179] Guenther G C, Green J E.Improved depth selection in the bathymetric swath survey system (BS3) combined offline processing (COP) algorithm[R].Rockville, MD: National Oceanic and Atmospheric Administration, Dept.of Commerce, 1982.
[180] HUANG Xianyuan, ZHAI Guojun, SUI Lifen, CHAI Hongzhou. Study on the detection of abnormal sounding data based on LS-SVM [J].Acta Oceanologica Sinica, 2010, 29(6):115-120.
[181] IHO Committee.Manual on Hydrography [M].Monaco: International Hydrographic Bureau, 2005.
[182] IHO Standards for Hydrographic Surveys, Special Publication No.44[S].4th.International Hydrographic Bureau, 1996.
[183] IHO Standards for Hydrographic Surveys, Special Publication No.44[S].5th.International Hydrographic Bureau, 2008.
[184] Jakobsson M, Calder B, Mayer L.On the effect of random errors in gridded bathymetric compilations [J].Journal of geophysical research, 2002, 107(B12):14.1-14.11.
[185] Kammerer E,Charlot D,Guillaudeux S,et al.Comparative Study of Shallow Water Multibeam Image for Cleaning Bathymetry Sounding Errors[C].OCEANS 2001 MTS/IEEE Conference and Exhibition.Honolulu,HI,USA:2001:2124-2128.
[186] K.-M. Chung, W.-C. Kao, T. Sun, L.-L. Wang and C.-J. Lin,“Radiusmargin bounds for support vector machines with the RBF kernel,”NeuralComputation, 2003 ,15(11):2643-2681.
[187] Legleiter C J, Kyriakidis P C.Spatial prediction of river channel topography by kriging [J].Earth Surface Processes and Landforms, 2008, 33(6):841-867.
[188] Lirakis C B,KPB.Automated Multibeam Data Cleaning and Target Detection[C].OCEANS2000 MTS/IEEE Conference and Exhibition.Providence,RI,USA:2000:719-723.
[189] Ma J S,James Theiler,Simon Perkins.Accurate On-line Support Vector Regression [J].Neural Computation,2003,15(11):2683-2703.
[190] Mallace D, Robertson P.Alternative Use of CUBE: How to Fit a Square Peg in a Round Hole[C].U.S.Hydrographic Conference.Norfolk, Virginia: 2007.
[191] Massimiliano Pontil, Alessandro Verri. Properties of support vector machines [J] Neural Comput, 1998.
[192] N. Cristianini and J. Shawe-Taylor, An introduction to Support VectorMachines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press, 2000.
[193] Suykens JAK,De Brabanter J,Lukas L,et a1.Weighted Least Squares Support Vector Machines:Robustness and Sparse Approximation [J].Neurocomputing,2002,48(1-4):85-105.
[194] Suykens JAK, Van Gestel T, Baestaens D,et a1. Least square support vector machines[M].World Scientific,Singapore,2002,265.
[195] Suykens JAK, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Process Letters, 1999, 9(3):293-300
[196] Suykens JAK. Least Squares Support Vector Machines for Classification and Nonlinear Modeling [J].Neural Network World, 2000, 10(1):29-48.
[197] S Amari, S Wu. Improving support vector machine classifier by modifying kernel function [J] Neural Networks, 1999.
[198] Van Gestel T,Suykens JAK,Baestaens D,et a1.Financial Time Series Prediction Using Least Squares Support Vector Machines within the Evidence Framework[J].IEEE Trans on Neural Networks,2001,12(4):809-821.
[199] Vapnik V.Statistical Learning Theory [M].New York: John Wiley, 1998.399.
[200] Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer,1995.
[201] Vapnik V.S Golowich, A J Smola. Support vector method for function approximation, regression, and signal processing [C] in: NIPS 9, San, 1997.