Accuracy Improvements in Linguistic Fuzzy Modeling

✍ Scribed by Jorge Casillas, Oscar Cordón, Francisco Herrera, Luis Magdalena (auth.), Dr. Jorge Casillas, Dr. Oscar Cordón, Dr. Francisco Herrera, Dr. Luis Magdalena (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2003
• Tongue: English
• Leaves: 392
• Series: Studies in Fuzziness and Soft Computing 129
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XII
Front Matter....Pages 1-1
Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview....Pages 3-24
Front Matter....Pages 25-25
COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy....Pages 27-45
Constrained optimization of genetic fuzzy systems....Pages 46-71
Trade-off between the Number of Fuzzy Rules and Their Classification Performance....Pages 72-99
Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms....Pages 100-118
Fuzzy CoCo: Balancing Accuracy and Interpretability of Fuzzy Models by Means of Coevolution....Pages 119-146
On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes....Pages 147-162
Front Matter....Pages 163-163
Linguistic Hedges and Fuzzy Rule Based Systems....Pages 165-192
Automatic Construction of Fuzzy Rule-Based Systems: A trade-off between complexity and accuracy maintaining interpretability....Pages 193-219
Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy....Pages 220-245
Front Matter....Pages 247-247
A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms....Pages 249-276
An Iterative Learning Methodology to Design Hierarchical Systems of Linguistic Rules for Linguistic Modeling....Pages 277-301
Learning Default Fuzzy Rules with General and Punctual Exceptions....Pages 302-337
Integration of Fuzzy Knowledge....Pages 338-365
Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?....Pages 366-385

✦ Subjects

Language Translation and Linguistics; Artificial Intelligence (incl. Robotics); Complexity; Economic Theory; Operation Research/Decision Theory