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. 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. Using GA’s to optimize the Membership Functions of The Type-1 Fuzzy System and Type-2 Fuzzy System we can improve the results of the fuzzy systems. We show in this paper the results of a type-2 approach for response integration that improves performance over the type-1 logic approaches.
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Lopez, M., Melin, P., Castillo, O. (2008). Optimization of Response Integration with Fuzzy Logic in Ensemble Neural Networks Using Genetic Algorithms. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70812-4_8
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