Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 32; no. 5; pp. 612 - 621
Main Authors: Abonyi, J., Babuska, R., Szeifert, F.
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2002
Subjects:
Algorithms
Clustering
Clustering algorithms
Cybernetics
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Gaussian
Input variables
Mathematical models
Multidimensional systems
Nonlinear systems
Optimization methods
Partitioning algorithms
Predictive models
Takagi-Sugeno model
ISSN:1083-4419
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:1083-4419
DOI:10.1109/TSMCB.2002.1033180