Interval type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimisation with genetic algorithms Online publication date: Wed, 04-Jun-2008
by Denisse Hidalgo, Oscar Castillo, Patricia Melin
International Journal of Biometrics (IJBM), Vol. 1, No. 1, 2008
Abstract: In this paper a comparative study of fuzzy inference systems as methods of integration in Modular Neural Networks (MNNs) for multimodal biometry is presented. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimised with simple genetic algorithms. 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 the fuzzy systems automatically. Then the response integration of the MNN was tested with the optimised fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behaviour of the two different integration methods of MNNs for multimodal biometry.
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