A neural fuzzy controller learning by fuzzy error propagation
Detlef D. Nauck, Rudolf Kruse · 1992
In this paper we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work in [2]. We solve this problem by definining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error. 1 Introduction One of the design problems of a fuzzy controller is the choice of appropriate membership functions or the tuning of a priori membership functio...