Adaptive Control of Nonlinear System Based on SVM Online Algorithm
The training of Support Vector Machine (SVM) is an optimization problem of quadratic programming which can not be applied to the online training in real time applications or time-variant data source. The online algorithms proposed by other researchers are with high computational complexity and slow training speed. This manuscript combines the projection gradient and adaptive natural gradient. It proposes the constraint projection adaptive natural gradient online algorithm for SVM regression. An adaptive SVM controller is designed in the state feedback control for a class nonlinear system. In order to demonstrate the availability of this adaptive SVM controller,we give a simulation of the simple nonlinear system. The results of simulation demonstrate this SVM online algorithm controller is very effective and the SVM controller can achieve a satisfactory performance
SUN Zonghai HU Ming LIU Hua
College of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）