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

Yi-jian LIU; Yan-jun FANG; Xue-mei ZHU

doi:10.1631/jzus.C0910176

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

人工智能 | Updated：2022-08-31

- Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data
- Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data
- 信息与电子工程前沿（英文） 2010年11卷第1期页码：56-62
- Affiliations：
  
  1.School of Electric & Automation Engineering, Nanjing Normal University, Nanjing 210042, China
  2.Department of Automation, Wuhan University, Wuhan 430072, China
- Author bio：
  
  E-mail:liuyijian_2002@163.com
- Funds：
- DOI：10.1631/jzus.C0910176
  中图分类号：
- 收稿：2009-03-29，
  
  修回：2009-09-29，
  
  录用：2009-08-17，
  
  纸质出版：2010-01
- Accepted：
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Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data[J]. 信息与电子工程前沿（英文）, 2010,11(1):56-62.

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]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(1): 56-62.
Modeling of hydraulic turbine systems based on a Bayesian-Gaussian neural network driven by sliding window data[J]. 信息与电子工程前沿（英文）, 2010,11(1):56-62. DOI： 10.1631/jzus.C0910176.

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]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(1): 56-62. DOI： 10.1631/jzus.C0910176.

摘要

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.

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