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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
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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     
Received: 29 March 2009      Published: 30 November 2009
CLC:  TP18  
Fund:  Project  (Nos. 60704024 and 60772107) supported by the  National Natural Science Foundation of China
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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/10.1631/jzus.C0910176     OR     http://www.zjujournals.com/xueshu/fitee/Y2010/V11/I1/56


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

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 
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