Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems
EXPLAINABLE AI AND OTHER APPLICATIONS OF FUZZY TECHNIQUES, NAFIPS 2021(2022)
摘要
The probability mixture structure of additive fuzzy systems allows uniform convergence of the generalized probability mixtures that represent the if-then rules of one system or of many combined systems. A new theorem extends this result and shows that it still holds uniformly for any continuous function of such fuzzy systems if the underlying functions are bounded. This allows fuzzy rule-based systems to approximate a far wider range of nonlinear behaviors for a given set of sample data and still produce an explainable probability mixture that governs the rule-based proxy system.
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