Divergence effects for online adaptation of membership functions

作     者：Hofbauer, A Heiss, M 

作者机构：Univ Technol Vienna Vienna Austria Siemens AG Villach Austria PSE NLT2 ECANSE A-1040 Vienna Austria 

出 版 物：《INTELLIGENT AUTOMATION AND SOFT COMPUTING》 (智能自动控制与软计算)

年 卷 期：1998年第4卷第1期

页      面：39-51页

核心收录：

学科分类：08[工学] 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：fuzzy control learning control basis functions spikes back-propagation algorithm 

摘      要：The adaptation of membership functions in a fuzzy system is a nonlinear optimization problem. Thus, the convergence of online learning algorithms is questionable. We demonstrate the convergence problems by analyzing two types of spikes, the narrow basis function spikes and the non-monotonic basis function spikes, which can occur during the online adaptation. Further, we show how these spikes can be avoided by restricting the parameter variations of the widths and the distances of the membership functions. According to these restrictions we have to conclude that in most cases it is better solely to adapt the rule conclusions than to adapt the membership functions.