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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (1): 56-62    DOI: 10.1631/jzus.C0910176
    
Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data
Yi-jian LIU*,1, Yan-jun FANG2, Xue-mei ZHU1
1 School of Electric & Automation Engineering, Nanjing Normal University, Nanjing 210042, China 2 Department of Automation, Wuhan University, Wuhan 430072, China
Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data
Yi-jian LIU*,1, Yan-jun FANG2, Xue-mei ZHU1
1 School of Electric & Automation Engineering, Nanjing Normal University, Nanjing 210042, China 2 Department of Automation, Wuhan University, Wuhan 430072, China
 全文: PDF(337 KB)  
摘要： 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.
关键词： Bayesian-Gaussian neural network (BGNN);  Hydraulic turbine;  Modeling;  Sliding window data    
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.
Key words: Bayesian-Gaussian neural network (BGNN)    Hydraulic turbine    Modeling    Sliding window data
收稿日期: 2009-03-29 出版日期: 2009-11-30
CLC:  TP18  
基金资助: Project  (Nos. 60704024 and 60772107) supported by the  National Natural Science Foundation of China
通讯作者: Yi-jian LIU     E-mail: liuyijian_2002@163.com
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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. Front. Inform. Technol. Electron. Eng., 2010, 11(1): 56-62.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910176    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I1/56

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