Parameters optimization and implementation of mixed kernels ε-SVM based on improved PSO algorithm

This paper constructed a mixed-kernel e-SVM by combining RBF kernel and polynomial kernel linearly in order to use SVM to predict more effectively and overcome some typical shortcomings(like weak generalized performance and learning capability of normal SVM).As there were many problems existing in standard PSO in the process of the latter stage,such as serious concussion,inclination-tendency and the high possibility of being involved in local maximum value,this paper proposed an improved PSO algorithm to solve the above problems at the same time as well as its mathematic model,and gave algorithmic procedure.It imported the following-factor of random-particle's maximum value to the expressions of momentum and the velocity of normal PSO algorithm,then back embedded the new momentum expression to the newly updated velocity formular,by which particle could weaken the concussion and inclination tendency simultaneously.A function simulation and a real-data based experiment prove that the mixed kernel e-SVM based on improved PSO presented in this paper has good advantages over many other predicting algorithms in forecasting precision,convergence velocity,robustness and simplicity,therefore,it's a valuable regression algorithm to be spread.