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CLC number: TP18

On-line Access: 2009-11-30

Received: 2009-03-29

Revision Accepted: 2009-08-17

Crosschecked: 2009-09-29

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.1 P.56-62

http://doi.org/10.1631/jzus.C0910176


Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data


Author(s):  Yi-jian LIU, Yan-jun FANG, Xue-mei ZHU

Affiliation(s):  School of Electric & Automation Engineering, Nanjing Normal University, Nanjing 210042, China; more

Corresponding email(s):   liuyijian_2002@163.com

Key Words:  Bayesian-Gaussian neural network (BGNN), Hydraulic turbine, Modeling, Sliding window data


Yi-jian LIU, Yan-jun FANG, Xue-mei ZHU. Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data[J]. Journal of Zhejiang University Science C, 2010, 11(1): 56-62.

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author="Yi-jian LIU, Yan-jun FANG, Xue-mei ZHU",
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pages="56-62",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910176"
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%A Yan-jun FANG
%A Xue-mei ZHU
%J Journal of Zhejiang University SCIENCE C
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%DOI 10.1631/jzus.C0910176

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T1 - Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data
A1 - Yi-jian LIU
A1 - Yan-jun FANG
A1 - Xue-mei ZHU
J0 - Journal of Zhejiang University Science C
VL - 11
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SP - 56
EP - 62
%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910176


Abstract: 
In this paper, a novel bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimization algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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JIA<toanny@126.com>

2010-01-29 10:37:09

Very interesting.

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