Title :
Study of nonlinear system identification based on support vector machine
Author :
Zhang, Ming-Guang ; Yan, Wei-Wu ; Yuan, Zhan-Ting
Author_Institution :
Sch. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., China
Abstract :
System identification plays an important role in control field. Support vector machine (SVM) is a novel machine learning method, and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima. SVM has high generalization. In this paper, nonlinear system identification based on SVM was discussed and corresponding simulation was implemented. Cross validation method is used to select hyperparameter of SVM model. Good result indicates that SVM is effective tool for nonlinear system identification.
Keywords :
generalisation (artificial intelligence); identification; learning (artificial intelligence); nonlinear systems; support vector machines; SVM model; cross validation method; generalization; hyperparameter selection; machine learning method; nonlinear system identification; support vector machine; Control systems; Lagrangian functions; Learning systems; Nonlinear systems; Power system modeling; Signal processing algorithms; Statistical learning; Support vector machine classification; Support vector machines; System identification;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1378604
