Nonlinear system identification using adaptive Chebyshev neural networks

摘要：

A new adaptive Chebyshev neural networks (ACNN) algorithm for the purpose of complex nonlinear system identification was proposed. In the proposed algorithm, the activation function of hidden units was defined by Chebyshev polynomials in the neural networks. The efficient algorithm for complex nonlinear system identification was constructed, which integrated Chebyshev neural networks with adaptive learning strategy to improve the identification accuracy and convergence rate. Furthermore, the networks algorithm was improved so that the applications becomed extensive. Then the ACNN directly learned dynamic characters of nonlinear system and identified it. The simulation results show that the ACNN algorithm have much less computation and high accuracy in the problem of complex nonlinear system identification.

关键词： Chebyshev polynomials neural networks adaptive learning strategy nonlinear system identification

作者: Mu Li Yigang He Mu Li

作者单位: College of Electrical and Information Engineering Hunan University Changsha, China College of Information and Electrical Engineering Hunan University of Science and Technology Xiangta

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 243-247

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）