Constructing a fuzzy expert system using the ILFN network and the genetic algorithm

A method for automatic construction of a fuzzy expert system from numerical data using the ILFN network and a genetic algorithm is presented. The incremental learning fuzzy neural (ILFN) network was developed for pattern classification problems. The ILFN network is a fast, one-pass, on-line, and incremental learning algorithm. A knowledge base for fuzzy expert systems is extracted from the hidden units of the ILFN classifier. The genetic algorithm is then used, in an iterative manner, to reduce the number of rules and select important input pattern features needed to generate a comprehensible fuzzy rule-based system.