Abstract
This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (a) learns and tunes a fuzzy logic controller even when only weak reinforcement such as a binary failure signal, is available; (b) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (c) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (d) learns to produce real-valued control actions
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