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Research on the Optimized Support Vector Regression Machines Based on the Differential Evolution Algorithm

The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. And the parameters selection is a procedure of global optimization search. Since the Differential Evolution (DE) population-based algorithm is a real- coding optimal algorithm with powerful global searching capacity, a hybrid model of DE-SVR based on the standard SVR model and DE algorithm is proposed in this paper. And then, the new hybrid implementation was applied to the short range regression prediction of the chaotic time series. At last, the experiment results showed the effectiveness of this approach and the better performance in searching time, compared with the conventional parameters searching approach of grid algorithm. algorithm has been achieved fine application effects in neural network training, filter design, cluster analysis. And this paper proposed a hybrid model based on the DE algorithm and the Support Vector Regression machine (SVR) model, which is called DE-SVR here. The proposed model is then applied to the short range regression forecast of the chaotic time series of Chens. The results of the proposed DE-SVR approach are compared with the results obtained by the conventional grid searching method and the PSO algorithm. Results presented show that the proposed DE-SVR approach appears to in higher prediction accuracy and faster searching.