Optimization of type-2 fuzzy integration in modular neural networks using an evolutionary method with applications in multimodal biometry

Abstract

We describe in this paper a new 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 and rules) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using type-1 and type-2 fuzzy inference systems. © 2009 Springer-Verlag Berlin Heidelberg.