Modified support vector regression for nonlinear control system modeling and its application

Aiming at nonlinear control system on-line modeling, the effects of training data distribution to the performance of SVR are analyzed, and a new modeling method based on modified support vector regression (SVR) is proposed. The analyzed results indicate that the data near to the new added sample should be preserved when training data are updated, which can improve the performance; the fixed parameters of SVR can not fit the entire on-line modeling process because the distribution is time-varying, and SVR may have a substandard performance when the system output changes in a small range. Therefore, three data updating criteria are proposed, and the width of the Gaussian kernel is set according to the correlation of training data in sampling time. The method is employed in multichannel electrohydraulic force servo synchronous loading system to predict the load output during 1280 sampling periods, the training time and the prediction mean absolute percentage error are 20.3387s and 0.357% respectively, and the results show its effectiveness.