Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure
type: Item
instance of: scholarly article
title: Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure
zbMATH Open document ID: 0997.93006
DOI: 10.1016/S0020-0255(01)00141-4
author: R. Smith
published in: Information Sciences
publication date: 2002-06-23T00:00:00Z
review text: A fuzzy modeling involving genetic algorithms combined with a new conciseness measure is described. This measure is nothing but the entropy \(-p\ln p-(1-p)\ln(1-p)\) first introduced in the literature by the Nobel winner Fermi and renamed later in the fuzzy literature. This entropy is used to evaluate the shape of a membership function. Nevertheless, since it could not distinguish between similarly shaped membership functions, one proposes a new measure for the deviation of a membership function from symmetry. Numerical results are displayed which show that the conciseness measure is in conflict with the accuracy of fuzzy models, and which the author has previously applied to fuzzy modeling using genetic algorithms.
Mathematics Subject Classification ID: 93A30
Mathematics Subject Classification ID: 94D05
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 1758116
zbMATH Keywords: fuzzy modeling
zbMATH Keywords: genetic algorithms
zbMATH Keywords: conciseness measure
zbMATH Keywords: entropy
zbMATH Keywords: membership function
reviewed by: Guy Jumarie
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1758116