Fuzzy Entropy Based Combined Learning Algorithm for Neural Networks

作     者：Min Yao (Dept. of Computer Science, Hangzhou University, Hangzhou 310028,P. R. China ) 

作者机构：Dept. of Computer Science Hangzhou University Hangzhou 310028 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：1996年第7卷第1期

页      面：15-22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)] 081104[工学-模式识别与智能系统] 08[工学] 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）] 

主　　题：Artificial neural networks Combined learning Fuzzy entropy criterion. 

摘      要：Learning is one of key problems of artificial neural networks. In this paper, we present a kind of combined learning algorithm based on fuzzy entropy criterion for neural networks. The basic idea is to simulate the learning mechanism of human brain and overcome the limitations of monocrifsterion learning. The comparison is made between the given learning algorithm and the typical BP algorithm in order to show the characteristics of the new algorithm.