title: Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes.
abstract: 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.
authors.affiliation: Faculty of Electrical Engineering Tržaška 25, Ljubljana, Slovenia. dejan.dovzan@fe.uni-lj.si
authors.forename: Dejan
authors.initials: D
authors.lastname: Dovžan
authors.forename: Igor
authors.initials: I
authors.lastname: Skrjanc
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D004738:Engineering / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D016018:Least-Squares Analysis; D017711:Nonlinear Dynamics; D010100:Oxygen / Q000032:analysis; D014865:Waste Disposal, Fluid / Q000706:statistics & numerical data
chemical_list: D010100:Oxygen