Impulse force based ART network with GA optimization

The different effects of input attributes on category results in supervised ART (adaptive resonance theory) network is quite important during the predictive stage in the application that was ignored by the traditional researches. In fact, some of the attributes have larger effect than the others on category results, but, even for the experts in that field, it is difficult to evaluate the effect. In this paper we present a novel supervised ART network namely impulse force based ART (IFART) network. It enhances the prediction accuracy of the supervised ART network by using genetic algorithm optimized impulsive forces on attributes. Then some experiments on benchmark data sets are given to show its good performance.