Identification of Industrial Processes Based on Support Vector Machines

Industrial processes are generally time varied and nonlinear, and it is difficult to acquire data for them. Support vector machine (SVM) provides a new mode for industrial processes identification due to its excellent learning capability. In this paper, SVM is applied to the identification of continuous stirred tank reactor (CSTR). Compared with BP neural network, the simulation results show the effectiveness and superiority of SVM.