Supervised learning in fuzzy systems: Algorithms and computational capabilities

The author presents model structures for fuzzy systems and accompanies these model structures with learning algorithms. The emphasis is on basic principles of the design, operating characteristics, and adaptation of fuzzy systems. Several supervised learning algorithms for the adjustment of parameters are discussed. Results of simulations of function approximation and system identification demonstrate that the model structures and supervised learning algorithms suggested for fuzzy systems are practically feasible