Title :
Multi-step-ahead optimal learning strategy for local model networks with higher degree polynomials
Author :
Banfer, O. ; Kampmann, G. ; Nelles, O.
Author_Institution :
Dept. of Mech. Eng., Univ. of Siegen, Siegen, Germany
fDate :
June 29 2011-July 1 2011
Abstract :
The idea of a learning strategy extension for nonlinear system identification with local polynomial model networks is presented in this paper. Usually the polynomial model tree (POLYMOT) algorithm utilizes a one-step-ahead optimal learning strategy. A demonstration example shows that this greedy behavior is not the best choice to reach a satisfying global model. Thus this strategy should be enlarged to a multi step-ahead optimal learning. Therefore, it is possible to find the optimal global model in a special case.
Keywords :
learning (artificial intelligence); nonlinear control systems; polynomials; POLYMOT algorithm; greedy behavior; local polynomial model network; multi-step-ahead optimal learning strategy; nonlinear system identification; one-step-ahead optimal learning strategy; polynomial model tree algorithm; Approximation algorithms; Approximation methods; Computational modeling; Nonlinear systems; Partitioning algorithms; Polynomials; Training;
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4577-0080-4
DOI :
10.1109/ACC.2011.5991241
