Fuzzy ARTMAP achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks. Fuzzy ARTMAP realizes a new minimax learning rule that conjointly minimizes predictive error and maximizes code compression or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or hidden units, to meet accuracy criteria. Improved prediction is achieved by training the system several times using different orderings of the input set, and then voting. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Simulations illustrated fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithmic systems.
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