Abstract:

The kernel function in the Support Vector Regression (SVR) machine has a great influence on the quality of model.Currently,however,every kernel has its advantages and disadvantages.Based on the fact that the regression accuracy and generalization performance of the SVR models depends on a proper setting of its parameters,the continuous multiant colony optimization (MACO) method based on gridding partition is applied in mixturekernels SVR parameters.The crossvalidation error is used as the fitness function of MACO.The optimal values in ant system were reflected by the 5 parameters of SVR.Simulation results show that the optimal selection approach based on MACOSVR has good robustness and strong global search capability.The method used for the research of modeling in the traffic flow forecast obtains higher accuracy than the models constructed with the Genetic Algorithm.

Key words: ant colony optimization;support vector regression machine;kernel function;parameter optimization