Efficient selection of inputs for artificial neural network models

MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings

Volumn , Issue , 2005, Pages 1806-1812

  Fernando, T M K G ^a   Maier, H R ^a   Dandy, G C ^a   May, R ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Artificial neural networks; Input identification; Mutual information; Outliers; Robust statistics

Indexed keywords

ALGORITHM IMPLEMENTATION; ARTIFICIAL NEURAL NETWORK MODELS; BOOTSTRAP METHOD; COMPLEX NONLINEAR SYSTEM; COMPUTATIONALLY EFFICIENT; CONFIDENCE INTERVAL; CONFIDENCE LIMIT; DATA DRIVEN TECHNIQUE; DATA SETS; FREE PARAMETERS; HIGH-DIMENSIONAL; HYDROLOGICAL PROCESS; INPUT SELECTION; INPUT VARIABLE SELECTION; INPUT VARIABLES; LINEAR CORRELATION COEFFICIENT; MODEL DEVELOPMENT; MODEL INPUTS; MUTUAL INFORMATIONS; NATURAL SYSTEMS; NON-LINEAR RELATIONSHIPS; NONLINEAR DATA; NONLINEAR PROCESS; ORIGINAL ALGORITHMS; OUTLIER DETECTION; OUTLIERS; PREDICTIVE PERFORMANCE; REAL SYSTEMS; RIVER MURRAY; ROBUST STATISTICS; ROBUST TECHNIQUE; SELECTION OF THE BEST; SOUTH AUSTRALIA; STOPPING CRITERIA; PARTIAL MUTUAL INFORMATIONS;

ALGORITHMS; COMPUTER SIMULATION; DECISION MAKING; FUZZY NEURAL NETWORKS; NEURAL NETWORKS; STATISTICS; COMPLEX NETWORKS; EFFICIENCY;

COMPUTATIONAL EFFICIENCY;

EID: 61749103449     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Bowden, G.J., Maier, H.R. and Dandy, G.C. (2005), Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river, Journal of Hydrology, 301, 93-107 (Pubitemid 39645411)
2. Bowden, G.J., Maier, H.R. and Dandy, G.C. (2002), Optimal division of data fro neural network models in water resources applications, Water Resources Research, 38(2), 10.1029/2001 WR000266
3. Chernick, M.R. (1999), Bootstrap Methods: A Practitioner's Guide, Wiley, New York
4. Davies, L. and Gather, U. (1993), The identification of multiple outliers, Journal of the American Statistical Association, 88 (423), 782-792
5. Fraser, A.M. and Swinney, H.L. (1986), Independent coordinates for strange attractors for mutual information, Physical Review A, 33(2), 1134-1140
6. Pearson, R.K. (2001), Exploring process data, Journal of Process Control, 11, 179-194
7. Phoon, K.K., Islam, M.N., Liaw, C.Y. and Liong, S.Y. (2002) Practical Inverse Approach for Forecasting Nonlinear Hydrological Time Series, Journal of Hydrological Engineering, 7(2), 116-128 (Pubitemid 34254138)
8. Maier, H.R. and Dandy, G.C. (1996), The use of artificial neural network for the prediction of water quality parameters, Water Resources Research, 32(4), 1013-1022
9. Moon, Y.I., Rajagopalan, B. and Lall, U. (1995), Estimation of mutual information using kernel density estimators, Physical Review E, 52(3), 2318-2321
10. Scott, D.W. (1992), Multivariate Density Estimation: Theory, Practice and Visualisation, Wiley, New York
11. Sharma, A. (2000), Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1- A strategy for system predictor identification, Journal of Hydrology, 239, 232-239
12. Silverman, B.W. (1986), Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York
13. Specht, D.F. (1991), A General Regression Neural Network, IEEE Transactions on Neural Networks, 2(6), 568-576