An input-output clustering method for fuzzy system identification

Di Wang, Xiao Jun Zeng, John A. Keane

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Clustering algorithms are often used for fuzzy system identification. However, most clustering algorithms do not consider the outputs for clustering. In addition, these algorithms do not consider how to obtain the optimal number of clusters. Without the optimal number of clusters, the final set of clusters may be inappropriate. To address this, this paper presents an Input-Output Clustering (IOC) algorithm to determine both the correct number of clusters and the appropriate location for them by considering both inputs and outputs. The proposed algorithm, when used for fuzzy system identification, achieves better performance than existing clustering methods. This performance is illustrated by examples of function approximation and dynamic system identification.

Original languageBritish English
Title of host publication2007 IEEE International Conference on Fuzzy Systems, FUZZY
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom
Duration: 23 Jul 200726 Jul 2007

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2007 IEEE International Conference on Fuzzy Systems, FUZZY
Country/TerritoryUnited Kingdom
CityLondon
Period23/07/0726/07/07

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