Fault Diagnosis Based on WNNs with Parameters Optimization by Immune Evolutionary Particle Swarm Algorithm

摘要：

The immune evolutionary mechanism of artificial immune system is used into Particle Swarm Optimization(IEPSO). A new training algorithm in wavelet neural networks(WNNs) based on IEPSO is presented, it can avoid early ripe of PSO and traditional BP algorithm. In the course of optimizing the parameters of WNNs, new algorithm use the immune evolutionary principle to improve the process of PSO, it determines the probability of their choice based on the size of fitness and concentration in antibodies, and dynamically adjusted crossover probability and mutation probability by use of fitness function. With the parameters optimized by IEPSO, the convergence performance of the WNNs is improved. The fault diagnosis of progressing cavity pumps well shows that the WNNs optimized by IEPSO can give higher recognition accuracy than the normal WNNs.

关键词： immune evolutionary:Particle Swarm Optimization (PSO):wavelet neural networks:fault diagnosis

作者: Yang Lu Weijian Ren Deping Gao Hongli Dong

作者单位: College of Information Technology,Heilongjiang Bayi Agricultural University,Daqing 163319 China. College of Electrical and Information Engineering,Daqing Petroleum Institute,Daqing 163318 China. information engineering department,ShanDong Polytechinc Vocational College,Jining 272017 China. Space Control and Inertial Technology Research Center,Harbin Institute of Technology,Harbin 150001 C

会议类型: 国际会议

会议名称: The 3rd International Symposium on Systems and Control in Aeronautics and Astronautics(第三届航空航天系统与控制国际会议 ISSCAA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 107-111

在线出版日期: 2010-01-08（万方平台首次上网日期，不代表论文的发表时间）