Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems

Abstract

Discusses the concept of intelligent expert systems and suggests tools for building an adaptable, in an online or in an off-line mode, rule base during the system operation in a changing environment. It applies evolving fuzzy neural networks (EFuNNs) as associative memories for the purpose of dynamic storing and modifying a rule base. Algorithms for rule extraction and rule insertion from EFuNNs are explained and applied to a case study using gas furnace data and the iris data set.