Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture. II. Implementation and application

This paper is the second of two companion papers. The foundations of the proposed method of heuristic constraint enforcement on membership functions for knowledge extraction from a fuzzy/neural architecture was given in Part I. Part II develops methods for forming constraint sets using the constraints and techniques for finding acceptable solutions that conform to all available a priori information. Moreover, methods of integration of enforcement methods into the training of the fuzzy-neural architecture are discussed. The proposed technique is illustrated on a fuzzy–AND classification problem and a motor fault detection problem. The results indicate that heuristic constraint enforcement on membership functions leads to extraction of heuristically acceptable membership functions in the input and output spaces. Although the method is described on a specific fuzzy/neural architecture, it is applicable to any realization of a fuzzy inference system, including adaptive and/or static fuzzy inference systems.