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Generating fuzzy rules by learning from examples

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2 Author(s)
L. -X. Wang ; Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA ; J. M. Mendel

A general method is developed to generate fuzzy rules from numerical data. The method consists of five steps: divide the input and output spaces of the given numerical data into fuzzy regions; generate fuzzy rules from the given data; assign a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; create a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and determine a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:22 ,  Issue: 6 )