计算机工程与应用Issue(18)：108-111,4.DOI:10.3778/j.issn.1002-8331.1305-0287
基于CEPSO-LSSVM的煤炭消费量预测模型
Coal consumption prediction based on LSSVM optimized by Catfish Particle Swarm Optimization algorithm
摘要
Abstract
The coal consumption has time-varying and nonlinear characteristics. In order to improve the prediction accuracy of coal consumption, a coal consumption prediction model based on Catfish Particle Swarm algorithm and Least Squares Support Vector Machine(CEPSO-LSSVM)is proposed. LSSVM parameter is encoded into the position of the particle, and minimum of the cross validation error of network training set is taken as optimal target, and then the parameters of LSSVM are obtained by the exchange information among particles, and“catfish effect”is introduced to keep the diversity of particle swarm to overcome the local optimum of the traditional particle swarm optimization algorithm, and coal consumption prediction model is built according to the optimum parameters, and the simulation test is carried out on actual coal consumption data. The results show that, compared with other prediction models, the proposed model can get better parameters, and coal consumption prediction accuracy can be improved. It is more suitable for complex coal consumption prediction.关键词
煤炭消费量/最小二乘支持向量机/粒子群优化算法/鲶鱼效应Key words
coal consumption/Least Squares Support Vector Machine(LSSVM)/Particle Swarm Optimization(PSO)algorithm/catfish effect分类
信息技术与安全科学引用本文复制引用
杨世杰,龙丹,周庆标..基于CEPSO-LSSVM的煤炭消费量预测模型[J].计算机工程与应用,2013,(18):108-111,4.基金项目
浙江省教育技术研究规划课题（No.JB083）。 （）
