Dynamic nonlinear state-space model with a neural network via improved sequential learning algorithm for an online real-time hydrological modeling

Journal of Hydrology

Volumn 468-469, Issue , 2012, Pages 11-21

  Hong, Yoon Seok Timothy ^a  

^a LONDON SOUTH BANK UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural network; Dynamic nonlinear state space model; Real time hydrological modeling; River temperature prediction; Sequential extended Kalman filtering (EKF) learning algorithm

Indexed keywords

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEMS; COMPLEX DYNAMICS; EXTENDED KALMAN FILTERING; HYDROLOGICAL DATA; HYDROLOGICAL MODELING; MODEL PERFORMANCE; MULTI LAYER PERCEPTRON; NOISE COVARIANCE; NONLINEAR STATE SPACE MODELS; ONLINE SIMULATION; OPERATIONAL DATA; POWER STATION; REAL-TIME TASKS; RIVER TEMPERATURE; SEQUENTIAL LEARNING ALGORITHM; THERMAL POWER STATIONS;

COMPUTATIONAL EFFICIENCY; COMPUTER SIMULATION; COVARIANCE MATRIX; EXTENDED KALMAN FILTERS; HYDROLOGY; LEARNING ALGORITHMS; NEURAL NETWORKS; THERMOELECTRIC POWER PLANTS;

DYNAMIC MODELS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COOLING WATER; COVARIANCE ANALYSIS; DISCHARGE; FUZZY MATHEMATICS; HYDROLOGICAL MODELING; NONLINEARITY; NUMERICAL MODEL; PREDICTION; REAL TIME; WATER TEMPERATURE;

EID: 84867099470     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.08.001     Document Type: Article

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