NDLI: Bioprocess soft sensor modeling using SVR based on improved PSO

Content Provider	IEEE Xplore Digital Library
Author	Dawei Zhou Guohai Liu Congli Mei Xingke Jiang Haixia Xu
Copyright Year	2010
Description	Author affiliation: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China (Dawei Zhou; Guohai Liu; Congli Mei; Xingke Jiang; Haixia Xu)
Abstract	In order to solve the problems of slow convergence and high complexity in dealing with large number of samples when modeling a bioprocess soft sensor using SVR, an improved PSO is used to solve the quadratic optimization problem to determine support vectors and the weights of them in this paper. By introducing constraint relationship in parameters, PSO could converge to the optimal point faster. Simulation results demonstrate that the proposed method could be simple to implement as a bioprocess soft sensor and has better convergence, which is presents as an alternative to current SVR training methods.
Starting Page	2942
Ending Page	2946
File Size	803818
Page Count	5
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Support vector machines Convergence Optimization Kernel Biological system modeling quadratic optimization soft sensor SVR PSO
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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