Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes

In this paper we propose a new approach to on-line Takagi–Sugeno fuzzy model identification. It combines a recursive fuzzy c -means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey–Glass time series with other...

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Bibliographic Details
Published in:ISA transactions 2011-04, Vol.50 (2), p.159-169
Main Authors: DOVZAN, Dejan, SKRJANC, Igor
Format: Article
Language:English
Subjects:
Adaptative systems
Algorithms
Applied sciences
Cluster Analysis
Clustering
Computer science
control theory
systems
Computer Simulation
Control theory. Systems
Diagnostic systems
Engineering - statistics & numerical data
Exact sciences and technology
Fuzzy
Fuzzy Logic
Fuzzy set theory
Least-Squares Analysis
Mathematical models
Modelling and identification
Nonlinear Dynamics
On-line systems
Online recursive identification
Optimal control
Oxygen - analysis
Recursive
Recursive fuzzy [formula omitted]-means clustering
Recursive fuzzy identification
Robotics
Waste Disposal, Fluid - statistics & numerical data
Waste water
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Summary:In this paper we propose a new approach to on-line Takagi–Sugeno fuzzy model identification. It combines a recursive fuzzy c -means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey–Glass time series with other established on-line identification methods. We showed that the developed algorithm gives a comparable degree of accuracy to other algorithms. The proposed algorithm can be used in a number of fields, including adaptive nonlinear control, model predictive control, fault detection, diagnostics and robotics. An example of identification based on a real data of the waste-water treatment process is also presented.
ISSN:0019-0578
1879-2022
DOI:10.1016/j.isatra.2011.01.004