DocumentCode
3140576
Title
The identification of industrial processes based on SVM
Author
Li, Li-na ; Hou, Chao-Zhen
Author_Institution
Dept. of Autom. Control, Beijing Inst. of Technol., China
Volume
1
fYear
2002
fDate
2002
Firstpage
520
Abstract
The Support Vector Machine (SVM) is a kind of novel machine learning method, which displays excellent learning capability. SVM also provides a new way for industrial process identification. Industrial processes generally are time varying, nonlinear and difficult to model with traditional methods. In this paper, SVM is used for the identification of the continuous stirred tank reactor (CSTR). The simulation results show the effectiveness and superiority of SVM.
Keywords
identification; learning (artificial intelligence); learning automata; nonlinear systems; process control; time-varying systems; continuous stirred tank; function fitting problems; industrial process identification; industrial processes control systems; learning capability; machine learning method; nonlinear regression; simulation results; support vector machine; time varying nonlinear processes; Chaos; Constraint optimization; Continuous-stirred tank reactor; Kernel; Learning systems; Linear regression; Machine learning; Neural networks; Nonlinear equations; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN
0-7803-7508-4
Type
conf
DOI
10.1109/ICMLC.2002.1176810
Filename
1176810
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