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Indexed by:期刊论文

Date of Publication:2008-01-01

Journal:NEURAL NETWORK WORLD

Included Journals:EI、SCIE、Scopus

Volume:18

Issue:6

Page Number:459-467

ISSN No.:1210-0552

Key Words:Fuzzy perception; learning algorithm; fuzzily separable; finite convergence

Abstract:This paper considers a fuzzy perceptron that has the same topological structure as the conventional linear perceptron. A learning algorithm based on a fuzzy 6 rule is proposed for this fuzzy perceptron. The inner operations involved in the working process of this fuzzy perceptron are based on the max-min logical operations rather than conventional multiplication and summation, etc. The initial values of the network weights are fixed as 1. It is shown that each network weight is non-increasing in the training process and remains unchanged once it is less than 0.5. The learning algorithm has an advantage, as proved in this paper, that it converges in a finite number of steps if the training patterns are fuzzily separable. This result generalizes a corresponding classical result for conventional linear perceptrons. Some numerical experiments for the learning algorithm are provided to support our theoretical findings.