Journal
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
Volume 70, Issue 1, Pages 26-52Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcss.2004.05.001
Keywords
fuzzy rule; information matrix; information diffusion; additive fuzzy system; nonlinear function
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In this paper, we use the information matrix technique to extract fuzzy if-then rules from data including noise. With a normal diffusion function, we change all crisp observations of a given sample into fuzzy sets to make an information matrix. We extract rules according to the centroids of the rows of an information matrix. These rules are integrated into an additive fuzzy system with the same rule weight. Such fuzzy systems can be used as adaptive function approximators. Simulations show that this method is very effective compared with the conventional least-squares method and neural network. The best advantage of the suggested method is that, it may be the simplest way to extract fuzzy if then rules from data. (C) 2004 Elsevier Inc. All rights reserved.
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