Robust fuzzy Gustafson–Kessel clustering for nonlinear system identification
- 1 November 2003
- journal article
- research article
- Published by Informa UK Limited in International Journal of Systems Science
- Vol. 34 (14-15), 787-803
- https://doi.org/10.1080/00207720310001655515
Abstract
This paper deals with Takagi–Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson–Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input–output data. The EGK algorithm can automatically determine the ‘optimal’ number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally-weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.Keywords
This publication has 10 references indexed in Scilit:
- A robust competitive clustering algorithm with applications in computer visionIeee Transactions On Pattern Analysis and Machine Intelligence, 1999
- Fuzzy Modeling for ControlInternational Series in Intelligent Technologies, 1998
- Clustering by competitive agglomerationPattern Recognition, 1997
- Fuzzy Model Identification Based on Cluster EstimationJournal of Intelligent & Fuzzy Systems, 1994
- Generation of Fuzzy Rules by Mountain ClusteringJournal of Intelligent & Fuzzy Systems, 1994
- Construction of fuzzy models through clustering techniquesFuzzy Sets and Systems, 1993
- Fitting an unknown number of lines and planes to image data through compatible cluster mergingPattern Recognition, 1992
- Input-output parametric models for non-linear systems Part I: deterministic non-linear systemsInternational Journal of Control, 1985
- Fuzzy identification of systems and its applications to modeling and controlIEEE Transactions on Systems, Man, and Cybernetics, 1985
- Robust StatisticsWiley Series in Probability and Statistics, 1981