Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting

Water Resources Research

Volumn 35, Issue 8, 1999, Pages 2591-2596

  Maier, Holger R ^a   Dandy, Graeme C ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ERROR ANALYSIS; FEEDFORWARD NEURAL NETWORKS; MATHEMATICAL MODELS; SALINITY MEASUREMENT; FORECASTING; NEURAL NETWORKS; NUMERICAL ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; WATER RESOURCES;

EXTENDED DELTA-BAR-DELTA (EDBD) ALGORITHMS; GENERALIZED DELTA (GD) RULE; NORMALIZED CUMULATIVE DELTA (NCD) RULE; CONNECTION WEIGHTS; EMPIRICAL - COMPARISONS; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FIRST ORDER METHOD; GENERALIZATION ABILITY; OPTIMAL MODEL PERFORMANCE; PREDICTION ERRORS; SECOND-ORDER ALGORITHMS;

WATER RESOURCES; ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; HYDROLOGICAL MODEL; SALINITY; WATER RESOURCE;

AUSTRALIA; MURRAY RIVER; SOUTH AUSTRALIA;

EID: 0032855262     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/1999WR900150     Document Type: Article

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