Pattern classification using fuzzy adaptive learning control network and reinforcement learning

In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a fuzzy adaptive learning control network (FALCON-R). FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). Thorough performance evaluation using Fisher´s Iris data is presented and compared against a novel FALCON-ART network. We show that the system can converge faster, is able to escape from local minima, and has excellent disturbance rejection capability and has strengths as a pattern classification technique.