Title :
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
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;
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
DOI :
10.1109/CICA.2009.4982781
