Tang W H,Wu Q H．Condition monitoring and assessment of power transformers using computational intelligence [M]．New York：Springer-Verlag Press,2011：95-104．
[2]
张德明．变压器分接开关状态监测与故障诊断[M]．北京：中国电力出版社,2008,121-142．Zhang Deming．Transformer tap condition monitoring and fault diagnosis[M]．Beijing：China Electric Power Press,2008：121-142(in Chinese)．
[3]
朱德恒,严璋,谈克雄,等．电气设备状态监测与故障诊断技术[M]．北京：中国电力出版社,2009：244-302．Zhu Deheng,Yan Zhang,Tan Kexiong,et al．Electrical equipment condition monitoring and fault diagnosis[M]．Beijing：China Electric Power Press,2009：244-302(in Chinese)．
[4]
Sheng Weifei,Xiao Binzhang．Fault diagnosis of power transformer based on support vector machine with genetic algorithm[J]．Expert Systems with Applications,2009,36(8)：11352-11357．
[5]
董明,孟源源,徐长响,等．基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J]．中国电机工程学报,2003,23(7)：88-92．Dong Ming,Meng Yuanyuan,Xu Changxiang,et al．Fault diagnosis model for power transformer based on support vector machine and dissolved gas analysis[J]．Proceedings of the CSEE,2003,23(7)：88-92(in Chinese)．
[6]
Tipping M E．The relevance vector machine[C]//Advances in Neural Information Processing Systems12．Denver,Colorado,USA：NIPS Foundation,2000：652-658．
[7]
Bishop C M,Tipping M E．Variational relevance vector machines[C]//Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence．Stanford,California,USA：Stanford University,2000：46-53．
[8]
Tipping M E．Sparse Bayesian learning and the relevance vector machine[J]．Journal of Machine Learning Research,2001,1(1)：211-244．
[9]
何创新,李彦明,刘成良,等．基于滑动平均与相关向量机的齿轮早期故障智能诊断[J]．振动与冲击,2010,29(12)：89-92．He Chuangxin,Li Yanming,Liu Chengliang,et al．Incipient fault diagnosis based on moving average and relevance vector machine[J]．Journal of Vibration and Shock,2010,29(12)：89-92(in Chinese)．
[10]
杨成福,章毅．相关向量机及在说话人识别应用中的研究[J]．电子科技大学学报,2010,39(2)：311-315．Yang Chengfu,Zhang Yi．Study to speaker recognition using RVM[J]．Journal of University of Electronic Science and Technology of China,2010,39(2)：311-315(in Chinese)．
[11]
陶新民,徐晶,杜宝祥,等．基于相空间RVM的轴承故障检测方法[J]．振动与冲击,2008,27(10)：6-10．Tao Xinmin,Xu Jing,Du Baoxiang,et al．Bearing fault detection based on RVM using phase space[J]．Journal of Vibration and Shock,2008,27(10)：6-10(in Chinese)．
[12]
Demir B,Erturk S．Hyperspectral image classification using relevance vector machines[J]．IEEE Geoscience and Remote Sensing Letters,2007,4(4)：586-590．
[13]
Gholami B,Haddad W M,Tannenbaum A R．Relevance vector machine learning for neonate pain intensity assessment using digital imaging[J]．IEEE Transactions on Biomedical Engineering,2010,57(6)：1457-1466．
[14]
Damoulas T,Ying Y,Girolami M A,et al．Inferring sparse kernel combinations and relevance vectors：an application to subcellular localization of proteins[C]//Proceedings of the 7th International Conference on Machine Learning and Applications(ICMLA2008)．San Diego,USA：IEEE,2008,577-582．
[15]
Psorakis I,Damoulas T,Girolami M A．Multiclass relevance vector machines：an evaluation of sparsity and accuracy[J]．IEEE Transactions on Neural Networks,2010,21(10)：1588-1598．
[16]
Kohavi R．A study of cross-validation and bootstrap for accuracy estimation and model selection[C]//Proceedings of the 14th international joint conference on artificial intelligence(IJCAI)．Montreal,Canada：AAAI,1995,1137-1143．
[17]
Leisch F,Jain L C,Hornik K．Cross-validation with active pattern selection for neural network classifiers[J]．IEEE Transactions on Neural Network,1998,9(1),35-41．
[18]
Girolami M,Rogers S．Hierarchic bayesian models for kernel learning[C]// Proceedings of the 22nd International Conference on Machine Learning．Bonn,Germany：University of Bonn,2005,241-248．
[19]
Albert J,Chib S．Bayesian analysis of binary and polychotomous response data[J]．Journal of the American Statistical Association,1993,88(442)：669-679．
[20]
Damoulas T,Girolami M A．Probabilistic multi-class multi-kernel learning：On protein fold recognition and remote homology detection[J]．Bioinformatics,2008,24(10)：1264-1270．
[21]
Affenzeller M,Winkler S,Wagner S,et al．Genetic Algorithms and Genetic Programming：Modern Concepts and Practical Applications[M]．New York：CRC,2009,1-22．
[22]
刘东平,单甘霖,张岐龙,等．基于改进遗传算法的支持向量机参数优化[J]．微计算机应用,2010,31(5)：11-15．Liu Dongping,Shan Ganlin,Zhang Qilong,et al．Parameters optimization of support vector machine based on improved genetic algorithm[J]．Microcoputer Applications,2010,31(5)：11-15(in Chinese)．
[23]
尚万峰,赵升吨,申亚京．遗传优化的最小二乘支持向量机在开关磁阻电机建模中的应用[J]．中国电机工程学报,2009,29(12)：65-69．Shang Wanfeng,Zhao Shengdun,Shen Yajing．Application of LSSVM Optimized by genetic algorithm to modeling of switched reluctance motor[J]．Proceedings of the CSEE,2009,29(12)：65-69(in Chinese)．
