Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms

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 si...

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Bibliographic Details
Published in:Information sciences Vol. 179; no. 13; pp. 2123 - 2145
Main Authors: Hidalgo, Denisse, Castillo, Oscar, Melin, Patricia
Format: Journal Article
Language:English
Published: Elsevier Inc 13.06.2009
Subjects:
Hybrid intelligent systems
Neural networks
Pattern recognition
Type-2 fuzzy logic
Pattern recognition
Neural networks
Type-2 fuzzy logic
Hybrid intelligent systems
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary: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.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2008.07.013