基于最小二乘支持向量机的氧化铝浓度预测

doi:10.16182/j.issn1004731x.joss.201805029

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (5): 1844-1849.doi: 10.16182/j.issn1004731x.joss.201805029

基于最小二乘支持向量机的氧化铝浓度预测

崔桂梅¹, 杨海靳¹, 刘丕亮¹, 于凯²

1.内蒙古科技大学信息工程学院,内蒙古包头 014010;
2.内蒙古科技大学数理学院,内蒙古包头 014010

收稿日期:2016-07-16 修回日期:2016-12-22 出版日期:2018-05-08 发布日期:2019-01-03
作者简介:崔桂梅(1963-),女,河北保定,博士,教授,研究方向为控制理论及计算机控制;杨海靳(1989-),男,山西大同,硕士生,研究方向为优化过程控制。
基金资助:
国家自然科学基金(61164018),内蒙古自治区自然科学基金(2014MS0612)

Prediction of Alumina Density Based on LSSVM

Cui Guimei¹, Yang Haijin¹, Liu Piliang¹, Yu Kai²

1. School Of Information Engineering, Inner Mongolia University Of Science & Technology, Baotou 014010, China;;
2. School Of Mathematics Physics, Inner Mongolia University Of Science & Technology, Baotou 014010, China;

Received:2016-07-16 Revised:2016-12-22 Online:2018-05-08 Published:2019-01-03

摘要/Abstract

摘要： 建立基于种群活性粒子群算法优化最小二乘支持向量机参数的铝电解氧化铝浓度预测模型,根据铝电解生产工艺及历史数据的特点,确定模型的基本输入变量,解决了粒子群优化算法早熟收敛及最小二乘支持向量机参数确定周期长的问题。利用群活性加速度作为多样性测度,当群活性的加速下降时,对粒子的位置和速度分别执行进化操作用以改进标准PSO(Particle Swarm Optimization)算法;与标准的LSSVM(Least Squares Support Vector Machine)方法相比,提出的改进PSO-LSSVM的预测模型有效地提高预测精度且计算速度更快。

关键词: 铝电解, 氧化铝浓度, 最小二乘支持向量机, 粒子群算法

Abstract: The prediction model of alumina density based on the PSO algorithm with swarm activity to optimize LSSVM method is built. According to the production process characteristics of aluminum electrolysis and historical data, the input variables of the model is determined. It can solve these problems that Particle Swarm Optimization (PSO) algorithm is with the risk of premature convergence and least square support vector machine is time consuming with parameter selection. The method uses swarm activity as diversity index. When swarm activity is quickened to descend, evolution operation is added to modify the positions or velocities of particles to improve standard PSO algorithm. Study shows that, the improved PSO-LSSVM prediction method has better estimating performance and less computational time than the traditional LSSVM method.

Key words: aluminum electrolysis, alumina density, least squares support vector machine, particle swarm optimization

中图分类号:

TP391.9

崔桂梅, 杨海靳, 刘丕亮, 于凯. 基于最小二乘支持向量机的氧化铝浓度预测[J]. 系统仿真学报, 2018, 30(5): 1844-1849.

Cui Guimei, Yang Haijin, Liu Piliang, Yu Kai. Prediction of Alumina Density Based on LSSVM[J]. Journal of System Simulation, 2018, 30(5): 1844-1849.

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基于最小二乘支持向量机的氧化铝浓度预测

Prediction of Alumina Density Based on LSSVM

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