Robust Online Process Modeling Method Based on Non-Bias LSSVM
ZHOU Xin-Ran1,2, TENG Zhao-Sheng1, JIANG Xing-Jun3
1.College of Electrical and Information Engineering,Hunan University,Changsha 410082
2.School of Information Science and Engineering,Central South University,Changsha 410075
3.Department of Computer,Hunan Radio and TV University,Changsha 410004
The accuracy of least squares support vector machine(LSSVM) is influenced easily by gross errors and noises superimposed on value measurement of plant output when LSSVM is applied to the dynamic process online modeling directly. Aiming at that problem, robust online process modeling method using non-bias LSSVM is presented after the characteristics of sample sequence structure and noise action are analyzed. During the prediction period, abnormal measure data are recognized and recovered, and measure data containing noises are detected and rectified according to the relation between the predicting error and the set threshold value. Consequently, noises in samples are decreased,and online LSSVM tracks dynamics of process better. The modeling method is robust, and it decreases the effect of gross error and Gaussian white noise on the prediction accuracy of LSSVM to improve the prediction accuracy. The numerical simulation shows the validity and advantage of the proposed method.
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2010, Vol. 23 
