Accuracy Improvements in Linguistic Fuzzy Modeling

By: Jorge Casillas (Editor), O. CordÃ³n (Editor), Francisco Herrera Triguero (Editor)

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Hardcover | 4 June 2003

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Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas is linguistic fuzzy modeling, where the legibility of the obtained model is the main objective. This task is usually developed by means of linguistic (Mamdani) fuzzy rule-based systems. An active research area is oriented towards the use of new techniques and structures to extend the classical, rigid linguistic fuzzy modeling with the main aim of increasing its precision degree. Traditionally, this accuracy improvement has been carried out without considering the corresponding interpretability loss. Currently, new trends have been proposed trying to preserve the linguistic fuzzy model description power during the optimization process. Written by leading experts in the field, this volume collects some representative researcher that pursue this approach.

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Accuracy improvements to find the balance interpretability-accuracy in linguistic fuzzy modeling: an overview	p. 3
COR methodology: a simple way to obtain linguistic fuzzy models with good interpretability and accuracy	p. 27
Constrained optimization of genetic fuzzy systems	p. 46
Trade-off between the number of fuzzy rules and their classification performance	p. 72
Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms	p. 100
Fuzzy CoCo: balancing accuracy and interpretability of fuzzy models by means of coevolution	p. 119
On the achievement of both accurate and interpretable fuzzy systems using data-driven design processes	p. 147
Linguistic hedges and fuzzy rule based systems	p. 165
Automatic construction of fuzzy rule-based fuzzy systems: A trade-off between complexity and accuracy maintaining interpretability	p. 193
Using individually tested rules for the databased generation of inter-pretable rule bases with high accuracy	p. 220
A description of several characteristics for improving the accuracy and interpretability of the fuzzy rule learning algorithms	p. 249
An iterative learning methodology to design hierarchical systems of linguistic rules for linguistic modeling	p. 277
Learning default fuzzy rules with general and punctual exceptions	p. 302
Integration of fuzzy knowledge	p. 338
Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?	p. 366
Table of Contents provided by Blackwell. All Rights Reserved.

Accuracy Improvements in Linguistic Fuzzy Modeling

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