Fuzzy Expert Systems Based on Membership Functions and Fuzzy Rules

The prime focus in artificial intelligence is on creating machines that can exhibit some form of intelligence. Accurate mathematical models neither always exist nor can they be derived for all complex environments because the domain may not be thoroughly understood. The solution consists of constructing rules that apply when input values lie within certain designer-defined categories. The major disadvantage of existing approach lies in defining the categories, called membership functions, for each input parameter - i.e. use of this method requires some form of expert knowledge in order to define these membership functions. The efficiency or accuracy of the fuzzy system is then proportional to the designer's expertise in the application area. The knowledge base in an expert system can grow incrementally and can be updated dynamically; so that the performance of an expert system can be enhanced. In this paper, a general learning method is proposed as a framework for automatically deriving membership functions and fuzzy if then rules from a setoff given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, corresponding fuzzy inference procedure to process inputs is also developed.