COMBINATION OF PARTICLE-SWARM OPTIMIZATION WITH LEAST-SQUARES SUPPORT VECTOR MACHINE FOR FDTD TIME SERIES FORECASTING

摘要

A new combination of particle-swarm optimization (PSO) and the least-squares support vector machine (LS-SVM) technique for FDTD time-series forecasting is presented. In this paper, the PSO is extended to optimize the hyper parameter used in the LS-SVM algorithm. The numerical simulations demonstrate that the PSO method can efficiently obtain the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSOJLS-SVM method can improve the computational efficiency of the FDTD algorithm, as compared to the direct FDTD method.