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Fuzzy Set Based Neural Networks: Structure, Learning and Application
Walmir Caminhas*, Hermano Tavares**, Fernando Gomide** and Witold Pedrycz***
*Federal University of Minas Gerais
Department of Electrical Engineering 30000-970 Belo Horizonte, Minas Gerais, Brasil
**State University of Campinas Faculty of Electrical & Computer Engineering 13083-970 Campinas, Sao Paulo, Brazil
***University of Alberta Dept. of Electrical & Computer Engineering Edmonton, Canada T6G 2G7
Received:January 10, 1999Accepted:April 16, 1999Published:June 20, 1999
Keywords:Neural fuzzy networks, Fuzzy neuron models, Learning, Pattern classification
Abstract
We introduce a class of neural network constructed from fuzzy set models of neurons. The network has a multilayer, feed-forward structure whose units are modeled through triangular norms and co-norms, and weights within the unit interval. The neuron models provide a wide spectrum of design choices - a desirable feature whenever real-world applications are of concern. We focus on pattern classification problems to introduce main concepts and algorithms. The learning procedure does not need any information about derivatives - a very convenient feature within fuzzy set theory that makes the procedure efficient and fast. We provide procedures to construct the network, initialize weights properly, and automatically generate classes of membership functions. Knowledge is easily extracted from the network as ifthen rules. Computational examples demonstrate neurofuzzy network performance and efficiency. We conclude with remarks on computational complexity analysis and a prospectus for further developments.
Cite this article as:W. Caminhas, H. Tavares, F. Gomide, and W. Pedrycz, “Fuzzy Set Based Neural Networks: Structure, Learning and Application,” J. Adv. Comput. Intell. Intell. Inform., Vol.3 No.3, pp. 151-157, 1999.Data files:
