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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1176810
