Generating fuzzy rules by learning from examples
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21589860,p. 263-268
- https://doi.org/10.1109/isic.1991.187368
Abstract
A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series.<>Keywords
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