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

Journal of Zhejiang University: Science C

Volumn 11, Issue 1, 2010, Pages 56-62

  Liu, Yi Jian ^a   Fang, Yan Jun ^b   Zhu, Xue Mei ^a  

^a NANJING NORMAL UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Bayesian Gaussian neural network (BGNN); Hydraulic turbine; Modeling; Sliding window data

Indexed keywords

BAYESIAN; GAUSSIANS; MATRIX PARAMETERS; MODELING; MODELING PROBLEMS; NONLINEAR CHARACTERISTICS; OFF-LINE TRAINING; ONLINE MODELING; SIMULATION RESULT; SLIDING WINDOW; SWARM OPTIMIZATION ALGORITHMS; TRAINING PROCEDURES;

BAYESIAN NETWORKS; ELECTRIC BATTERIES; HIGH TEMPERATURE SUPERCONDUCTORS; HYDRAULIC MACHINERY; HYDRAULIC MOTORS; HYDRAULIC TURBINES; HYDRAULICS; NEURAL NETWORKS; OPTIMIZATION;

KNOWLEDGE BASED SYSTEMS;

EID: 77951209815     PISSN: 18691951     EISSN: 1869196X     Source Type: Journal    
DOI: 10.1631/jzus.C0910176     Document Type: Article

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