A Class of ANFIS Based Channel Equalizers for Mobile Communication Systems

Abstract

System modeling based on conventional mathematical tools is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system, employing if--then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. This {\em fuzzy identification} explored has found numerous practical applications in control, prediction and inference. However, no standard methods exist for optimally transforming human experience into the rule base of a fuzzy inference system. There is a need for effective methods for tuning the membership functions (MFs) so as to minimize the output error measure or maximize performance index. The Adaptive Network Based Fuzzy Inference System (ANFIS), which can serve as a basis for constructing a set of fuzzy if--then rules with appropriate membership functions to generate the stipulated input--output pairs, was proposed by J.S.R.Jang. We present a class of novel channel equalizers based on the ANFIS architecture.