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中文摘要：
针对直接利用最小二乘支持向量机（LSSVM）对动态过程在线建模时预测精度易受过程输出测量值上的粗大误差和噪声影响的问题,在分析样本序列结构特征和噪声作用特征基础上,提出一种基于无偏置项LSSVM的稳健在线过程建模方法.该方法在每一预测周期中根据预测误差与设定阈值之间的关系来识别和恢复异常测量值、识别和修正含噪声测量值,从而降低样本中的噪声,使得出的LSSVM较好地跟踪过程的动态特性.这种在线过程建模方法具有稳健性,能减少输出值上粗大误差和高斯白噪声对LSSVM预测精度的影响,提高预测精度.数字仿真显示该方法的有效性和优越性.
英文摘要：
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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