Publication

Abstract : System modelling based on conventional mathematical tools like differential equations is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system, employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. This fuzzy modeling or fuzzy identification explored by Takagi and Sugeno, has found numerous practical applications in control, prediction and inference. However there are some basic aspects of this approach which are in need of better understanding. More specifically, no standard methods exist for optimally transforming human knowledge or experience into the rule base and database 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. In this perspective, a novel architecture called 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, is taken up by Jang. We in this paper present a class of novel channel equalizers based on the ANFIS architecture.