大连理工大学学报
大連理工大學學報
대련리공대학학보
JOURNAL OF DALIAN UNIVERSITY OF TECHNOLOGY
2015年
1期
89-96
,共8页
高斯搜索%预测控制%最小二乘支持向量机%改进粒子群%磨矿过程
高斯搜索%預測控製%最小二乘支持嚮量機%改進粒子群%磨礦過程
고사수색%예측공제%최소이승지지향량궤%개진입자군%마광과정
Gaussian search%predictive control%least square support vector machine (LS-SVM)%improved particle swarm%grinding process
磨矿车间工业现场在保证控制效果的同时，一般要求控制变量具有较小的变化率。提出一种基于高斯搜索的改进粒子群优化算法，该算法以高斯分布来初始化粒子群，并改进粒子速度更新公式，将所提算法融合到最小二乘支持向量机预测控制中。针对选矿厂磨矿过程，给出了基于最小二乘支持向量机的预测控制系统，以及基于高斯搜索的改进粒子群优化算法步骤。对实际磨矿过程进行仿真实验，结果表明该算法在保证控制效果的同时，能大幅度减小控制量的变化率，具有良好的性能指标和应用前景。
磨礦車間工業現場在保證控製效果的同時，一般要求控製變量具有較小的變化率。提齣一種基于高斯搜索的改進粒子群優化算法，該算法以高斯分佈來初始化粒子群，併改進粒子速度更新公式，將所提算法融閤到最小二乘支持嚮量機預測控製中。針對選礦廠磨礦過程，給齣瞭基于最小二乘支持嚮量機的預測控製繫統，以及基于高斯搜索的改進粒子群優化算法步驟。對實際磨礦過程進行倣真實驗，結果錶明該算法在保證控製效果的同時，能大幅度減小控製量的變化率，具有良好的性能指標和應用前景。
마광차간공업현장재보증공제효과적동시，일반요구공제변량구유교소적변화솔。제출일충기우고사수색적개진입자군우화산법，해산법이고사분포래초시화입자군，병개진입자속도경신공식，장소제산법융합도최소이승지지향량궤예측공제중。침대선광엄마광과정，급출료기우최소이승지지향량궤적예측공제계통，이급기우고사수색적개진입자군우화산법보취。대실제마광과정진행방진실험，결과표명해산법재보증공제효과적동시，능대폭도감소공제량적변화솔，구유량호적성능지표화응용전경。
It is necessary for the industrial field of grinding plant to ensure the control effect and its control variables with less variation rate at the same time.An improved particle swarm optimization algorithm based on Gaussian search is proposed, where the particle swarm is initialized by the characteristics of Gaussian distribution,and the particle velocity update formula is modified.The proposed algorithm is combined with a least square support vector machine (LS-SVM )-based predictive control.Aiming at the grinding process of a concentration plant,a predictive control system based on LS-SVM is designed,and the steps of the improved particle swarm optimization algorithm are also provided.The results of simulation experiments on actual grinding process demonstrate that the proposed algorithm can greatly reduce the control variable changing rate while ensuring the control effect,and have good performance index and application prospects.
