Abstract
The two most essential fuzzy rule-based models used in the literature and in the industrial applications are briefly described. The way of reasoning in these models is shown. Interpolative reasoning for the case of sparse rule bases is also discussed. Rule base compression by eliminating redundant rules whose information can be reconstructed within a set accuracy interval from the remaining rules by using the previous interpolation method is shown. A general fuzzy model is discussed that contains the previous fuzzy models (as well as the nonfuzzy one) as special cases. Ways of transforming the different approximative models into each other via the general model and interpolation are presented.
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