Overview of fuzzified neural networks with comparison of learning mechanism

作     者：Kuo, Ching-Yi Wang, Hsiao-Fan 

作者机构：Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan 

出 版 物：《INTERNATIONAL JOURNAL OF FUZZY SYSTEMS》 (Int. J. Fuzzy Syst.)

年 卷 期：2008年第10卷第2期

页      面：71-83页

核心收录：

主　　题：fuzzy neural networks fuzzy arithmetic learning algorithm back-propagation 

摘      要：A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function of fuzzy neural networks, in this study, we focus on review and comparison of the existing learning algorithms, so that the theoretical achievement and the application agenda of each considered algorithm can be clarified from the aspects of computation complexity and accuracy. Two numerical examples of nonlinear mapping of fuzzy numbers and realization of fuzzy IF-THEN rules are used for illustration and analysis.