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

  • Authors:

  • Denisse Hidalgo;Oscar Castillo;Patricia Melin

  • Affiliations:

  • Tijuana Institute of Technology, Division of Graduate Studies and Research, Department of Computer Science, 22500 Tijuana BC, Mexico;Tijuana Institute of Technology, Division of Graduate Studies and Research, Department of Computer Science, 22500 Tijuana BC, Mexico;Tijuana Institute of Technology, Division of Graduate Studies and Research, Department of Computer Science, 22500 Tijuana BC, Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

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.