DocumentCode
2957120
Title
Estimating module relevance with Sugeno integration of modular neural networks using Interval Type-2 Fuzzy logic
Author
Mendoza, Olivia ; Melin, Patricia ; Licea, Guillermo
Author_Institution
Sch. of Eng. of UABC, Univ. of Tijuana, Tijuana
fYear
2008
fDate
1-8 June 2008
Firstpage
1329
Lastpage
1335
Abstract
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.
Keywords
fuzzy logic; fuzzy neural nets; fuzzy reasoning; image recognition; integration; type theory; Sugeno integration; fuzzy inference system; image recognition; interval type-2 fuzzy logic; modular neural network; module relevance estimation; Fuzzy logic; Hip; Integral equations; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location
Hong Kong
ISSN
1098-7576
Print_ISBN
978-1-4244-1820-6
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2008.4633970
Filename
4633970
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