摘要
针对氧化铝质量分数的建模与控制问题,提出一种新的基于最小二乘支持向量机(LS-SVM)和预测控制的建模与控制策略。首先,针对LS-SVM建模时的参数选取问题,提出一种基于混沌优化的CHAOS LS-SVM算法获得最优氧化铝质量分数预测模型。然后,提出一种基于LS-SVM的氧化铝质量分数预测控制算法,采用混沌优化在线求解最优控制律。仿真结果表明:CHAOS LS-SVM算法建立的氧化铝质量分数预测模型,其泛化能力要比基于神经网络(NN)的氧化铝质量分数预测模型的强;基于LS-SVM的氧化铝质量分数预测控制算法,其控制精度要比基于NN的氧化铝质量分数预测控制算法的高。
Considering the problem of alumina concentration modeling and control, a novel modeling and control strategy based on least squares support vector machine （LS-SVM） and predictive control were proposed. First, aiming at the problem of parameter selection of LS-SVM, a CHAOS LS-SVM algorithm based on chaos optimization was presented to obtain optimal alumina concentration prediction model. Then, an alumina concentration predictive control algorithm based on LS-SVM was developed, which uses chaos optimization to solve optimal control law online. The simulation results show that the generalization ability of alumina concentration prediction model established by CHAOS LS-SVM algorithm is stronger than that of alumina concentration prediction model based on neural network （NN）, and the control precision of alumina concentration predictive control algorithm based on LS-SVM is higher than that of alumina concentration predictive control algorithm based on NN.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第10期3917-3923,共7页
Journal of Central South University:Science and Technology
基金
北京市自然科学基金资助项目(4122022)
湖南省教育厅科研项目(11C0223)
关键词
氧化铝质量分数
最小二乘支持向量机
预测控制
混沌优化
alumina concentration
least squares support vector machine （LS-SVM）
predictive control
chaosoptimization
作者简介
通信作者:阎纲(1977-),男,湖南长沙人,博士研究生,从事系统建模与控制研究,电话:13467600560,E-mail:hnczyg@126.com
