Journal
INFORMATION SCIENCES
Volume 179, Issue 13, Pages 2123-2145Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2008.07.013
Keywords
Type-2 fuzzy logic; Pattern recognition; Neural networks; Hybrid intelligent systems
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We describe in this paper a comparative study between fuzzy inference systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms with the goal of having optimized versions of both types of fuzzy systems. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy systems of integration. The comparative study of the type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry. (C) 2008 Elsevier Inc. All rights reserved.
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