An Optimal Sparseness Approach for Least Square Support Vector Machine
Least square support vector machine(LSSVM)is a well accepted process modeling technique.However,it has an instinct shortcoming as that the solution is lack of spareness.In this paper,a particle swarm optimization(PSO)based optimal spareness approach for LSSVM is proposed and validated.The spareness of LSSVM is firstly formulated as an optimization problem,where pruning percentage of the training data set is taken as the optimization variable.And then,PSO is employed to solve the spareness problem.A LSSVM model for carbon content in fly ash of utility boiler is used for algorithm validation.Long term operation data of a 600MW boiler is collected for comparison studies.The presented results convince that the PSO based optimal spareness approach exceeds the classical method,and is capable of converging to an optimal support vector set.
LSSVM optimal sparseness PSO pruning percentage
Jia Luo Shihe Chen Le Wu Shirong Zhang
Electric Power Research Institute of Guangdong Power Grid Corporation,Guangzhou 510080,China Department of Automation,Wuhan University,Wuhan 430072,China
国际会议
长沙
英文
3621-3626
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）