Fuzzy Set Based Neural Networks: Structure, Learning and Application
Article 1999 en
Authors
WC
Walmir M. Caminhas
HT
Hermano Tavares
FG
Fernando Gomide
Abstract
1 min read
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.
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