A SUPERIOR TRAINING STRATEGY FOR THREE-LAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS

This research was partially supported by grants from the Hydrologic Research Laboratory of the U.S. National Weather Service (Grant no. NA37WH0385), the NASA -EOS Interdisciplinary Research Program (IDP -88 -086), and the NOAA Research Program (NA16RC0119 -0). The first author greatly appreciates the fellowship support provided by the NASA Global Change Program (Grant No. NGT- 30045).

[1]  Nazif Tepedelenlioglu,et al.  A fast new algorithm for training feedforward neural networks , 1992, IEEE Trans. Signal Process..

[2]  M. A. Styblinski,et al.  Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing , 1990, Neural Networks.

[3]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[4]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[5]  Soroosh Sorooshian,et al.  Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model , 1993 .

[6]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[7]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[8]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[9]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[10]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[11]  Q. Duan,et al.  A global optimization strategy for efficient and effective calibration of hydrologic models. , 1991 .

[12]  Tsu-Shuan Chang,et al.  A universal neural net with guaranteed convergence to zero system error , 1992, IEEE Trans. Signal Process..

[13]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[14]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[15]  Chao-Lin Chiu,et al.  Nonlinear time varying model of rainfall-runoff relation. , 1969 .

[16]  Ehud D. Karnin,et al.  A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.

[17]  Tom Tollenaere,et al.  SuperSAB: Fast adaptive back propagation with good scaling properties , 1990, Neural Networks.

[18]  Vijay P. Singh,et al.  Hydrologic Systems: Rainfall-Runoff Modeling , 1988 .

[19]  N. Crawford,et al.  DIGITAL SIMULATION IN HYDROLOGY' STANFORD WATERSHED MODEL 4 , 1966 .

[20]  D. H. Pilgrim Travel Times and Nonlinearity of Flood Runoff From Tracer Measurements on a Small Watershed , 1976 .

[21]  C. Charalambous,et al.  Conjugate gradient algorithm for efficient training of artifi-cial neural networks , 1990 .