Adaptation Of Vigilance Factor And Choice Parameter In Fuzzy ART System

In adaptive resonance theory network, the choice of vigilance factor (VF) and choice parameter. (CP) affects the performance of the network, such as the number of classes into which the data are classified. These parameters are typically chosen and adapted using human judgment, experience, and heuristic rules. Rather than choosing and optimizing these parameters manually, we use learning automata to automatically adapt these parameters. In an earlier paper [Bahri99] we examined the ability of P-model learning automata to only adapt the vigilance factor of fuzzy art network. In this paper we further study the effectiveness of LA in adaptation of VF and CP. This time we try to adapt both VF and CP simultaneously using different models of learning automata: P-model, Q-model, and Smodel. The feasibility of the proposed method is shown through simulation on three problems: the circle in the square, nested spirals, and gaussian distributed two groups.