[1]徐丽莎,钱晓山,阳春华.结合GM(1,1)和LSSVM的多效蒸发过程参数预测[J].智能系统学报,2012,7(05):462-466.
 XU Lisha,QIAN Xiaoshan,YANG Chunhua.Parameter prediction of multieffect evaporation process combining GM(1,1) with LSSVM[J].CAAI Transactions on Intelligent Systems,2012,7(05):462-466.
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结合GM(1,1)和LSSVM的多效蒸发过程参数预测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年05期
页码:
462-466
栏目:
出版日期:
2012-10-25

文章信息/Info

Title:
Parameter prediction of multieffect evaporation process combining GM(1,1) with LSSVM
文章编号:
1673-4785(2012)05-0462-05
作者:
徐丽莎1钱晓山23阳春华3
1.中南林业科技大学 涉外学院,湖南 长沙 410004;
2.宜春学院 物理科学与工程技术学院,江西 宜春336000; 
3.中南大学 信息科学与工程学院,湖南 长沙 410083
Author(s):
XU Lisha1 QIAN Xiaoshan23 YANG Chunhua3
1. Shewai College, Central South University of Forestry and Technology, Changsha 410004, China;
2. Physical Science and Technology College, Yichun University, Yichun 336000, China;
3. School of Information Science & Engineering, Central South University, Changsha 410083, China
关键词:
小波变换GM(11)模型LSSVM模型多效蒸发过程参数预测
Keywords:
wavelet transform GM (1 1) model LSSVM model multieffect evaporation process parameter prediction
分类号:
TP273
文献标志码:
A
摘要:
为了解决多效蒸发过程具有高噪声和非平稳等特性的参数时间序列预测问题,提出了一种基于小波变换结合GM(1,1)和LSSVM的蒸发过程参数预测方法.该方法首先利用Mallat算法对参数时间序列进行分解和重构,分离出序列中的低频信息和高频信息;然后对低频信息构建GM(1,1)模型,对高频信息则用最小二乘支持向量机进行拟合;最后将各模型的预测结果进行叠加,从而得到最终的预测结果.以氧化铝多效蒸发过程的生产数据进行了实验验证,结果表明,该预测算法切实可行且优于单一的GM(1,1)和LSSVM方法,具有较好的泛化性能和较强的鲁棒性,可用于氧化铝生产蒸发过程的优化控制.
Abstract:
A parameter prediction method was proposed for solving the timeseries prediction problem on the parameters of the multieffect evaporation process with high noise and nonstationary, combining GM (1, 1) and least squares support vector machines (LSSVM) based on the wavelet transform model. 〖JP3〗Firstly, the Mallat algorithm was used to decompose and reconstruct the time series of parameters, in order to separate low frequency and highfrequency sequence. Next, the GM (1, 1) model was designed by using a low frequency and highfrequency information sequence based on the LSSVM. Finally, a result of the prediction on all models was analyzed to determine the final prediction results. Production data of a multieffect evaporation process in alumina production were tested in the experiment; and the results show the prediction algorithm is feasible and superior to a single GM (1, 1). The test demonstrated the LSSVM method had a good generalization performance and powerful robustness; and could be used for operation of an optimal evaporation process in the alumina production.

参考文献/References:

[1]王晓兰,王明伟.基于小波分解和最小二乘支持向量机的短期风速预测[J].电网技术, 2010, 34(1): 179184. 
WANG Xiaolan, WANG Mingwei. Shortterm wind speed forecasting based on wavelet decomposition and least square support vector machine[J]. Power System Technology, 2010, 34(1): 179184.
[2]张华,任若恩.基于小波分解和残差GM(1,1)AR的非平稳时间序列预测[J].系统工程理论与实践, 2010, 30(6): 10161020. 
ZHANG Hua, REN Ruoen. Nonstationary time series prediction based on wavelet decomposition and remanet GM(1,1)AR[J]. Systems Engineering Theory and Practice, 2010, 30(6): 10161020.
[3]LI Derchiang, FANG Yaohwei. An algorithm to cluster data for efficient classification of support vector machines[J]. Expert Systems with Applications, 2008, 34(3): 20132018.
[4]COMAK E, ARSLAN A. A new training method for support vector machines: clustering kNN support vector machines[J]. Expert Systems with Applications, 2008, 35(3): 564568.
[5]KULKARNI A, JAYARAMAN V K, KULKARNI B D. Knowledge incorporated support vector machines to detect faults in Tennessee Eastman process[J]. Computers & Chemical Engineering, 2005, 29(10): 21282133. 
[6]MALLAT S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674693.
[7]胡昌华,李国华,刘涛,等.基于MATLAB 6.X的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社, 2004: 4549.
[8]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社, 2004: 1213.
[9]VAPNIK V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988999.
[10]SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293300.
[11]SUYKENS J A K. Optimal control by least squares support vector machines[J]. Neural Networks, 2001, 14(1): 2325.
[12]邓兴升.统计学习理论在大地测量中的应用[D].武汉:武汉大学, 2007: 6670. 
DENG Xingsheng. The application of statistical learning theory in geodesy[D]. Wuhang: Wuhang University, 2007: 6670.
[13]CRITINANINI N, SHAWETAYLOR J. An introduction to support vector machines and other kernelbased learning methods[M]. London, UK: Cambridge University Press, 2000: 2527.
[14]邓乃扬,田英杰.支持向量机——理论、算法与拓展[M].北京:科学出版社, 2004: 1517.

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备注/Memo

备注/Memo:
收稿日期: 2011-11-02.
网络出版日期:2012-09-07.
基金项目:国家自然科学基金资助项目(60874069). 
通信作者:钱晓山.
E-mail: qianxiaoshan@126.com.
作者简介:
徐丽莎,女,1984年生,讲师,主要研究方向为复杂工业过程建模、优化与控制、嵌入式系统. 
钱晓山,男,1980年生,讲师,博士研究生,主要研究方向为复杂工业过程建模、优化与控制. 
阳春华,女,1965年生,教授,博士生导师,博士,享受国家政府特殊津贴,中国有色金属学会计算机学术委员会秘书长,中国自动化学会理事、应用专业委员会委员、技术过程故障诊断与安全性专业委员会委员,中国人工智能学会智能控制与智能管理专业委员会委员,湖南省自动化学会常务理事.主要研究方向为复杂工业过程建模、优化控制、智能信息处理.完成或在研国家自然科学基金、国家“863”与“973”计划、国家高技术产业化等科研项目36项.曾获国家科技进步二等奖2项,省部级科技进步奖16项,教育部首届“新世纪优秀人才”,第5届湖南省青年科技奖,湖南省“十大杰出女性”.申请国家发明专利19项、授权6项,申请软件著作权8项,发表学术论文300余篇,其中被SCI、EI检索110余篇.
更新日期/Last Update: 2012-11-13