Plant identification and performance optimization for neuro-fuzzy networks

This paper discusses the structures and learning algorithms for identification and optimization with neuro-fuzzy networks (NFN). NFN are knowledge-based multilayer neural networks constructed by integrating three types of modular subnets for pattern recognition, fuzzy reasoning, and control synthesis, respectively. In this way, a NFN combines the reasoning procedure of fuzzy logic and learning capability of neural networks uniquely, thus it is able to incorporate linguistic knowledge in the form of fuzzy rules in its network structure and then refine this knowledge through training and self learning. Simulation results are presented here to illustrate these ideas.