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

摘要

The paper discusses the concept of intelligent expert systems and suggests tools for building adaptable, in an on-line or in an off-line mode, rule base during the system operation in a changing environment. It applies evolving fuzzy neuralnetworks 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.