Modelling of the Relaxation Least Squares-Based Neural Networks and Its Application

作     者：Lu Kongkuo, Chen Zengqiang & Yuan ZhuzhiDepartment of Automation, Nankai University, Tianjin 300071, P.R.China(Received July 5, 2001) 

作者机构：Department of Automation Nankai University Tianjin 300071 P.R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2002年第13卷第2期

页      面：16-21页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This project was supported by the National Natural Science Foundation of China (No. 60174021) the Key Project of Tianjin Natural Science Foundation (No.010115) 

主　　题：Backpropagation Learning systems Least squares approximations 

摘      要：A relaxation least squares-based learning algorithm for neual networks is proposed. Not only does it have a fast convergence rate, but it involves less computation quantity. Therefore, it is suitable to deal with the case when a network has a large scale but the number of training data is very limited. It has been used in converting furnace process modelling, and impressive result has been obtained.