Topology optimization of fuzzy systems for response integration in ensemble neural networks: The case of fingerprint recognition

Abstract

We describe in this paper a new method for response integration in ensemble neural networks with Type-1 and Type-2 Fuzzy Logic using Genetic Algorithms (GAs) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on its 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. Using GAs to optimize the fuzzy rules of The Type-1 and Type-2 Fuzzy System we can improve the results of the response integration. We show in this paper a comparative study of the results of a type-2 approach for response integration that improves performance over the type-1 fuzzy logic approaches.