Computer
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Soft computing for control of non-linear dynamical systems
Soft computing for control of non-linear dynamical systems
Soft Computing and Fractal Theory for Intelligent Manufacturing
Soft Computing and Fractal Theory for Intelligent Manufacturing
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Theoretical Advances and Applications of Fuzzy Logic and Soft Computing
Theoretical Advances and Applications of Fuzzy Logic and Soft Computing
Type-2 Fuzzy Logic: Theory and Applications
Type-2 Fuzzy Logic: Theory and Applications
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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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.