SUpervised HIerarchical CLUSTering (SUHICLUST) for nonlinear system identification

Author

Hartmann, Benjamin ; Nelles, Oliver ; Skrjanc, Igor ; Sodja, Anton

Author_Institution

Dept. of Mech. Eng., Univ. of Siegen, Siegen

fYear

2009

Firstpage

41

Lastpage

48

Abstract

In this paper the new algorithm SUHICLUST (supervised hierarchical clustering) is presented. It unifies the strengths of the supervised, incremental construction scheme LOLIMOT with the advantages of product space clustering. The result of this fusion is a powerful structure identification algorithm that enables approximation of processes with axes-oblique partitioning, high flexible validity functions and local polynomial models. The theoretical comparison with LOLIMOT and product space clustering and a demonstration example underline the usefulness of SUHICLUST.

Keywords

identification; learning (artificial intelligence); nonlinear systems; pattern clustering; polynomial approximation; trees (mathematics); approximation theory; axes-oblique partitioning; heuristic tree; high flexible validity function; incremental construction scheme; local polynomial model; nonlinear system identification; product space clustering; supervised hierarchical clustering; Automatic control; Clustering algorithms; Fuzzy logic; Heuristic algorithms; Interpolation; Mechatronics; Nonlinear systems; Partitioning algorithms; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on

Conference_Location

Nashville, TN

Print_ISBN

978-1-4244-2752-9

Type

conf

DOI

10.1109/CICA.2009.4982781

Filename

4982781