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CLC number: TP18
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Received: 2004-06-22
Revision Accepted: 2005-02-05
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Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm
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LIU Yi-jian, ZHANG Jian-ming, WANG Shu-qing. Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm[J]. Journal of Zhejiang University Science A, 2005, 6(10): 1026-1029.
@article{title="Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm",
author="LIU Yi-jian, ZHANG Jian-ming, WANG Shu-qing",
journal="Journal of Zhejiang University Science A",
volume="6",
number="10",
pages="1026-1029",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A1026"
}
%0 Journal Article
%T Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm
%A LIU Yi-jian
%A ZHANG Jian-ming
%A WANG Shu-qing
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 10
%P 1026-1029
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A1026
TY - JOUR
T1 - Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm
A1 - LIU Yi-jian
A1 - ZHANG Jian-ming
A1 - WANG Shu-qing
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 10
SP - 1026
EP - 1029
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A1026
Abstract: In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.
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