Superior training of artificial neural networks using weight-space partitioning

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 1997, Pages 1919-1923

  Gupta, H V ^a   Hsu, Kuo Lin ^a   Sorooshian, S ^a  

^a UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BACK PROPAGATION; DOWNHILL SIMPLICES; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; GLOBAL SEARCH ALGORITHM; LINEAR LEAST SQUARES; MULTISTART; THREE-LAYER;

ALGORITHMS; SURFACE RECONSTRUCTION; BACKPROPAGATION; FUNCTION EVALUATION; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; MONTE CARLO METHODS;

NEURAL NETWORKS; FEEDFORWARD NEURAL NETWORKS;

LINEAR LEAST SQUARE SIMPLEX (LLSSIM); WEIGHT SPACE PARTITIONING;

EID: 0030682699     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.614192     Document Type: Conference Paper

References (15)

1. D. E. Rumelharts, E. Hinton, and J. Williams, "Learning internal representation by error propagation," in Parallel Distributed Processing, vol. 1, pp. 318-362. Cambridge, MA: MIT Press, 1986.
2. S. Haykin, Neural Networks: A Comprehensive Foundation, New York, Macmillan College Pub. Company, 1994.
3. R. A. Jacob, "Increased rates of convergence through learning rate adaptation," Neural Networks, vol. 1, pp. 295-307, 1988.
4. T. Tollenaere, "SuperSAB: fast adaptive back propagation with good scaling properties," Neural Networks, vol. 3, pp. 561-573,1990.
5. T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink, and D. L. Alkon, "Accelerating the convergence of the back-propagation method," Biological Cybernetics, vol. 59, pp. 257-263,1988.
6. D. B. Parker, "Optimal algorithms for adaptive networks: second-order backpropagation, second-order direct propagation, and second order Hebbian learning," in Proc. 1st Int. Conf. Neural Networks, vol. 2, pp. 593-600, 1987.
7. R. Battiti, "First and second order methods for learning: between steepest desent and newton's method," Neural Computation, vol. 4, no. 2, pp. 141-166,1992.
8. C. Charalambous, "Conjugate gradient algorithm for efficient training of artificial neural networks," IEE Proceedings, vol. 139, no. 3,1992.
9. M. A. Styblinski, and T. S. Tang, "Experiments in nonconvex optimization: stochastic approximation with function smoothing and simulated annealing." Neural Networks, vol. 3, pp. 467-483, 1990.
10. Q. Duan, S. Sorooshian, and V. K. Gupta, "Effective and efficient global optimization for conceptual rainfall runoff models," Water Resour. Res., vol. 28, no. 4, pp.1015-1031, 1992.
11. Q. Duan, V. K. Gupta, and S. Sorooshian, "A shuffled complex evolution approach for effective and efficient global minimization, J. Optimiz. Theory Appl., vol. 73, no. 3, pp. 501-521, 1993.
12. R. S. Scalero and N. Tepedelenlioglu, "A fast new algorithm for training feedforward neural networks", IEEE Trans, on Signal Processing, vol. 40, no. 1,1992.
13. A. J. Nelder and R. Mead, "A simplex method for function minimization," Compu. J., vol. 7, no. 4, pp. 308-313,1965.
14. K. Hsu, H. V. Gupta, and S. Sorooshian, "Artificial neural network modeling of the rainfall-runoff process", Water Resour. Res., vol. 31, no. 10, pp. 2517-2530,1995.
15. R. C. Eberhart and R. W. Dobbins, Neural network PC tools: a practical guide, San Diego, CA: Academic Press, 1990.