The Application of Soft-sensing Based on LS_SVM within Free Search

被引：0
|
作者：
Liu, Zehua [1 ]
Yin, Rui [2 ]
Qin, Cheng [3 ]
机构：
[1] AVIC First Aircraft Inst, Dept Mech & Elect Syst, Xian 710089, Peoples R China
[2] Xian Feibao Technol Co Ltd, Prod & Mfg Dept, Xian 710089, Peoples R China
[3] AVIC First Aircraft Inst, Comm Sci & Technol, Xian 710089, Peoples R China
来源：
2018 IEEE CSAA GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC) | 2018年
关键词：
free search; LS_SVM; PSO; GA; grid-search arithmetic; soft-sensing;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the difficulty of parameter optimization in soft sensor model based on least squares support vector machine, we used free search algorithm to optimize it, so as to improve the accuracy of soft measurement. Taking the soft measurement of gas content in gas - liquid two phase flow as an example, in this paper, four intelligent optimize arithmetic (free search, PSO, GA and grid-search) were compared based on optimize precision. The experiment proves that, LS_SVM model based on free search gives better estimate precision and better generalization capabilities than the other three optimization methods. It provides a better prediction method for soft sensing.
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页数：4
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