Support vector machine based direct inverse-model identification
基于支持向量机的直接逆模型辨识

After a simple discussion of the principle of the inverse_model identification,a support vector machines(SVM) based direct inverse-model identification method is developed by using SVM's excellent ability of function approximation.According to the train data,linear and nonlinear systems' black-box identification is performed by using SVM with quadric polynomial and Gaussian RBF kernel respectively.Simulation results show that the performance of SVM based direct inverse-model is better than that of BP neural network in that it has better identification precision,quicker identification speed and stronger generalization ability.