A linear ridgelet network approach for fault diagnosis of analog circuit

作     者：XIAO YingQun& HE YiGang College of Electrical and Information Engineering, Hunan University, Changsha 410082, China 

作者机构：College of Electrical and Information Engineering Hunan University Changsha China 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2010年第53卷第11期

页      面：2251-2264页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China for Distinguished Young Scholar(Grant No. 50925727) the National Natural Science Foundation of China (Grant No. 60876022) the National High-Tech Research & Development Program of China (Grant No. 2006AA04A104) 

主　　题：fault diagnosis of analog circuit ridgelet linear ridgelet network wavelet packet decomposition 

摘      要：A linear ridgelet network combining ridgelet, linear term and the standard feed-forward neural network used for diagnosing the faults of analog circuit is constructed, and a training algorithm based on the steepest gradient descent method and momentum method for this network and the procedure for diagnosing these faults are proposed. The resulting linear ridgelet network can learn more rapidly from training samples and handle more effectively the complicated fault information of circuit under test than wavelet network and ridgelet network, classifying these faults effeciently and correctly and achieving a high classification accuracy. The simulation results demonstrate the effectiveness of this approach.