Nonlinear System Identification And Control Based On Support Vector Machine

Support vector machine (SVM) is a new machine learning technique which is development in the middle of 90's. Statistical Learning Theory (SLT) is foundation of SVM. SLT study statistical regulation and learning methods under small samples condition. Tradition statistics premise has enough samples. SVM is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima, and has high generalization. Because of its advantage on modeling of nonlinear system, SVMR became a new and strong tool in intelligent control field in recent years. Supporting by mathematics theory, SVMR nonlinear modeling and SVMR nonlinear control theory not only has a simple model, but also provides a new control theory which is suitable for complex nonlinear system.Firstly, this paper systematically studied basic theories and applications of the support vector machine. Various support vector machine methods that appear in the word are compared. Paper studies method that using support vector regression to identify nonlinear system. The effect of SVM kernel parameter and punish gene are discussed. In order to get the optimal parameters of SVM automatically, avoiding costs lots of time to select parameters, a SVM parameter selection approach based on fuzzy genetic algorithms is proposed in this paper. The nonlinear system identification is studied using the crossover probability p_c and mutation probability p_m of the on-line adjustment genetic algorithm based on fuzzy logic. Fuzzy genetic algorithms have rapider evolvement speed and higher precision than genetic algorithms. An effective method to solve SVM parameter selection is provided. This method is used in nonlinear system identification. The simulation results show that this method is very effective.Secondly, this paper studied theories of nonlinear system identification based on least square support vector machine (LS-SVM). Paper compares LS-SVM and SVM difference. In order to get the optimal parameters of LS-SVM automatically, use Fuzzy Genetic Algorithm to select parameters of LS-SVM, and use FGA-Least Square SVM to nonlinear calibration of temperature sensor. Experimental results show that this method has more accurate than CMAC network for nonlinear calibration of temperature sensor. This method is very effective.Finally, a predictive control algorithm based on least squares support vector machines (LS-SVM) model is...