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Estimating module relevance with Sugeno integration of modular neural networks using Interval Type-2 Fuzzy logic

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3 Author(s)
Mendoza, O. ; Sch. of Eng. of UABC, Univ. of Tijuana, Tijuana ; Melin, Patricia ; Licea, G.

In this paper a fuzzy logic approach to determine the relevance of each module in modular neural networks for images recognition is presented. The tests were made with Type-1 and Interval Type-2 Fuzzy Inference Systems, to compare the performance of the proposed approach. In both cases the fusion operator for the modules is the Sugeno Integral, and the estimated parameters are the fuzzy densities.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008