Interpretability issues in fuzzy modeling /

Interpretability issues in fuzzy modeling /

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Bibliographic Details
Imprint:	Berlin ; New York : Springer, ©2003.
Description:	1 online resource (xiv, 643 pages) : illustrations.
Language:	English
Series:	Studies in fuzziness and soft computing, 1434-9922 ; v. 128 Studies in fuzziness and soft computing ; v. 128.
Subject:	Intelligent control systems. Fuzzy systems. Soft computing. Fuzzy systems. Intelligent control systems. Soft computing.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11071959

Hidden Bibliographic Details
Other authors / contributors:	Casillas, J. (Jorge)
ISBN:	9783540370574 3540370579 9783642057021 3642057020 354002932X 9783540029328
Notes:	Includes bibliographical references and index.
Summary:	Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.
Other form:	Print version:
Standard no.:	10.1007/978-3-540-37057-4

Table of Contents:

Overview
Improving the Interpretability with Flexible Rule Stucture
Complexity Reduction in Linguistic Fuzzy Models
Complexity Reduction in Precise Fuzzy Models
Interpretability Constraints in TSK Fuzzy Rule-Based Systems
Assessments on the Interpretability Loss
Interpretation of Black-Box Models as Fuzzy Rule-Based Models.