Title :
Adaptive Control of Nonlinear Discrete-Time System by Least Square Support Vector Machine
Author :
Xu, Jian-Qiang ; Wang, Jian-Jun ; Zhu, Jun ; Chen, Shu-Zhong
Author_Institution :
Center of Mathematics and Physics Teaching, Shanghai Institute of Technology, Shanghai 200233, China; Department of Computer Science and Technology, East China Normal University, Shanghai 200062, China E-MAIL: jqxu@citiz.net
Abstract :
In this paper we introduce the use of recurrent least square support vector machine algorithm for the adaptive control of a class of nonlinear discrete-time systems. The curse of dimensionality is avoided by using the finite time window. Advantage of the newly designed algorithm is that the computation of inverse matrix is avoided. Simulation results also verify the effectiveness of the algorithm.
Keywords :
Nonlinear discrete-time system; adaptive control and least square support vector machine; Adaptive control; Algorithm design and analysis; Equations; Iterative algorithms; Least squares approximation; Least squares methods; Multi-layer neural network; Neural networks; Support vector machine classification; Support vector machines; Nonlinear discrete-time system; adaptive control and least square support vector machine;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527004
