Interpretability Issues in Fuzzy Modeling

Jorge Casillas

ISBN 10: 354002932X / ISBN 13: 9783540029328

Published by Springer Berlin Heidelberg, 2003

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783540029328

Bibliographic Details

• Title: Interpretability Issues in Fuzzy Modeling
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2003
• Language: English
• ISBN 10: 354002932X
• ISBN 13: 9783540029328
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 40D millimeters 241 millimeters width by 160 millimeters height by 40 millimeters depth
• Weight: 1144 grams

About this title

Synopsis

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.