[1]XIANG Zheng-rong,CHEN Qing-wei.An approach to soft sensor modeling based onwavelets and a least square support vector machine[J].CAAI Transactions on Intelligent Systems,2010,5(1):63-66.
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An approach to soft sensor modeling based onwavelets and a least square support vector machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 1 Page number: 63-66 Column: 学术论文—人工智能基础 Public date: 2010-02-25
Title:
An approach to soft sensor modeling based onwavelets and a least square support vector machine
Author(s):
XIANG Zheng-rong;  CHEN Qing-wei
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Keywords:
soft sensing;  least square support vector machine (LSSVM);  wavelet;  quantum particle swarm optimization
CLC:
TP274
DOI:
-
Abstract:
Some industrial process variables are very difficult to measure. To overcome this problem, a soft sensor modeling, based on wavelets and a least square support vector machine (LSSVM), was proposed. Initially, a stream of sample data was decomposed into subsequences with different frequences. This was done on the basis of wavelet transform. Then the respective subsequences were modeled by appropriate SVMs. Finally, estimated values for the primary variables were obtained by wavelet reconstruction. A quantum particle swarm optimization (QPSO) algorithm was employed to select parameters for the LSSVM and the kernel function. Simulation results confirmed that the proposed method has high precision and good generalization ability.
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