About the use of fuzzy clustering techniques for fuzzy model identification

In this work we present an alternative approach to generate fuzzy rules with a functional consequent associated to the TSK fuzzy model. In our case, using fuzzy clustering algorithms that look for linear behaviours in the product space of the input-output data, we analyse different methods to genera...

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Bibliographic Details
Published inFuzzy sets and systems Vol. 106; no. 2; pp. 179 - 188
Main Authors Gómez-Skarmeta, A.F., Delgado, M., Vila, M.A.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.09.1999
Elsevier
Subjects
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Fuzzy clustering
Fuzzy modelling
Mathematics
Modelling and identification
Multivariate analysis
Probability and statistics
Sciences and techniques of general use
Statistics
Unsupervised learning
Fuzzy modelling
Unsupervised learning
Fuzzy clustering
Functional
System identification
Fuzzy algorithm
Fuzzy set
Input output model
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Summary:In this work we present an alternative approach to generate fuzzy rules with a functional consequent associated to the TSK fuzzy model. In our case, using fuzzy clustering algorithms that look for linear behaviours in the product space of the input-output data, we analyse different methods to generate the associated fuzzy rules using in some cases multidimensional reference fuzzy sets in the product space of the input variables and in other cases fuzzy sets in each of the different dimensions. In any case the rules being generated correspond to a TSK fuzzy model.
ISSN:0165-0114
1872-6801
DOI:10.1016/S0165-0114(97)00276-5