A neuro-fuzzy approach to hybrid intelligent control

This paper presents a neuro-fuzzy approach to the development of high-performance real-time intelligent and adaptive controllers for nonlinear plants. Several paradigms derived from cognitive sciences are considered and analyzed in this work, such as neural networks, fuzzy inference systems, genetic algorithms, etc. The different control strategies are also integrated with finite-state automata, and the theory of fuzzy-state automata is derived from that. The novelty of the proposed approach resides in the tight integration of the control strategies and in the capability of allowing a hybrid design. Finally, three practical applications of the proposed hybrid approach are analyzed.