Comparative Study of Fuzzy Methods for Response Integration in Ensemble Neural Networks for Pattern Recognition

Abstract

We describe in this paper a new method for response integration in ensemble neural networks with Type-1 Fuzzy Logic and Type-2 Fuzzy Logic using Genetic Algorithms (GA’s) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints to the test proposed method of response integration. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on their biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. First we use GA’s to optimize the fuzzy rules of The Type-1 Fuzzy System and Type-2 Fuzzy System to test the proposed method of response integration and after using GA’s to optimize the membership function of The Type-1 Fuzzy Logic and Type-2 Fuzzy logic to test the proposed method of response integration and finally show the comparison of the results between these methods. We show in this paper a comparative study of fuzzy methods for response integration and the optimization of the results of a type-2 approach for response integration that improves performance over the type-1 logic approaches.