控制理论与应用2011，Vol.28Issue(12)：1825-1830,6.
热处理炉钢板温度度的的自适应混沌粒子群算法–最小二乘支持向量机优化预报算法
Optimized prediction algorithm with adaptive chaos particle swarm optimization-least squares support vector machine for steel plate temperature prediction in heat treatment furnace
摘要
Abstract
To deal with the difficulty in parameter adjustment and the low precision of the traditional heat-conduction model, we build a prediction model for the steel plate temperature, based on the least-squares-support-vector machine（LSSVM） which is optimized by the improved particle-swarm algorithm. First, on the basis of the particle-swarm algorithm, we propose an adaptive chaotic particle-swarm algorithm（ACPSO） for which the validity, robustness and the optimization efficiency are quantitatively evaluated based on performance indices; and then, the radial basis functions are selected as the kernel function. Thus, the temperature prediction model of steel plate is built with LSSVM and optimized with ACPSO algorithm. Finally, the model is simulated by using the data acquired from the site and used in practical operation; the result indicates that the prediction model based on ACPSO and LSSVM has higher prediction accuracy than the tradition one, achieving the goal of intelligent optimization.关键词
热处理炉/粒子群优化算法/支持向量机/混沌Key words
heat treating furnace/PSO（particle swarm optimizer algorithm）/SVM（support vector machine）/chaos分类
信息技术与安全科学引用本文复制引用
李静,王京,杨磊,刘森..热处理炉钢板温度度的的自适应混沌粒子群算法–最小二乘支持向量机优化预报算法[J].控制理论与应用,2011,28(12):1825-1830,6.基金项目
“十一五”国家科技支撑计划资助项目 （）
