Parameter optimization algorithm for support vector machine based on predatory search genetic algorithm
Based on the fact that generalization and fitting accuracy of the Support Vector Machine (SVM) model depend on its parameters setting, a predatory search genetic algorithm was proposed to determine the parameters of the SVM. The target of this algorithm was to minimize the fitting error of output and three parameters of SVM were used as the decision variables. An application example on the glutamic acid fermentation process shows that the method can improve the training accuracy and forecast accuracy of glutamate concentration and is an effective way to optimize the parameters of SVM.
NETL
NSTL
万方数据
维普
