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Optimization of type-2 fuzzy systems based on the level of uncertainty, applied to response integration in modular neural networks with multimodal biometry

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4 Author(s)
Hidalgo, D. ; Comput. Sci. in the Sch. of Eng., UABC Univ., Tijuana, Mexico ; Melin, P. ; Castillo, O. ; Licea, G.

In this paper we describe an evolutionary method for the optimization of a modular neural network for multimodal biometry. The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using optimal interval type-2 fuzzy inference systems. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty with application to fuzzy response integration.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010