Model selection of support vector regression using particle swarm optimization algorithm

作者：Yang HZ^*; Shao XG; Chen G; Ding F

来源：Dynamics of Continuous Discrete and Impulsive Systems: Series A - Mathematical Analysis , 2006, 13: 1417-1425.

• support vector regression
• model selection
• particle swarm optimization
• soft-sensor

A new automatic search methodology for model selection of support vector regression (SVR), based on the particle swarm optimization (PSO) algorithm, was proposed to search for the adequate hyper-parameters of SVR. In this method, each particle indicates a group of hyper-parameters, and the population is a collection of particles. Two artificial data experiments results show that our method performs Superiorly oil function approximation. Furthermore, the proposed method was applied to establish a soft-sensor model for average molecular weight in polyacrylonitrile productive process. The results of real data simulation also show that this method is effective.

• 出版日期2006-2
• 单位江南大学