Reduction methods of fuzzy inference rules with neural network learning algorithm

Describes reduction methods of the rule unit with fuzzy neural networks. The approaches are presented with a reducing mechanism of the rule unit which use three parameters, central value, width of the membership function in the antecedent part, and real number in the consequent part, constituted according to a fuzzy neural system. These methods indicate that a different technique exists besides the reduction approach. Experimental results are presented in order to show that the effectiveness is different in the proposed techniques for average inference error and learning iteration