关键词: 在线过程建模, 抗噪, 时变非线性过程, 最小二乘支持向量机

Abstract: Least Squares Support Vector Machine（LSSVM）’s predicting effect is worse in process modeling while it is employed directly in the presence of the process output measurement noise.In order to improve LSSVM’s predicting accurancy，an anti-noise online process modeling method based on non-bias LSSVM is presented.During per predicting step，plant output measuring error judgement is executed；the measuring value is revised，and the revised one is applied to formation of sample，if it differs from the predicting one seriously，consequently，the effects of noise overriding on the measuring value is reduced.The experimental results indicate that，the approach provides more accurate prediction than the direct LSSVM and existing weighted LSSVMs with the process output measurement in Gaussian white noise.

Key words: online process modeling, anti-noise, time-varying nonlinear process, Least Squares Support Vector Machine（LSSVM）