Identification Algorithm of Neural Network Based on Dynamic Generalized Objective Function

摘要：

　　To improve the identification accuracy and robustness to the peak and disorder noise of dynamic neural network learning algorithm,a new algorithm is presented whose objective function is constructed by combining a deterministic function to approximate the absolute value function with least square criteria,and recursive equations for weights training of output layer are derived using Gauss-Newton iterative algorithm without any simplification.Comparison with the Karayiannis method,the new algorithm has better robustness when disorder and peak noises exist in the training samples.Simulation results show the efficiency of the proposed method.

关键词： generalized objective function identification neural network

作者: LiuXinle Yang Hongliang Li Hongguo ZhouYilin

作者单位: Beijing Institute of Strength and Environment Engineering Beijing ,China

会议类型: 国际会议

会议名称: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference(IAEAC 2015)(2015 IEEE先进信息技术,电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 460-464

在线出版日期: 2015-12-19（万方平台首次上网日期，不代表论文的发表时间）