The support vector machine is applied to the identification of a typical time-varying, nonlinear industrial process, the continuous stirred reactor, and compared with the BP neural network modeling. The simulation results show the effectiveness and superiority of the support vector machine. The support vector machine, with its excellent learning ability, provides a new approach for the identification of industrial processes.
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