A study of possible improvements to the Alopex training algorithm

Proceedings - Brazilian Symposium on Neural Networks, SBRN

Volumn 2000-January, Issue , 2000, Pages 125-130

  Bia, A ^a  

^a UNIVERSITY OF ALICANTE   (Spain)

Author keywords

Annealing; Computer networks; Distributed computing; Energy measurement; Logistics; Neural networks; Probability; Stochastic processes; Temperature; Testing

Indexed keywords

ANNEALING; COMPUTER NETWORKS; DISTRIBUTED COMPUTER SYSTEMS; DISTRIBUTED PARAMETER NETWORKS; ELECTRIC POWER MEASUREMENT; LOGISTICS; NEURAL NETWORKS; PROBABILITY; RANDOM PROCESSES; STOCHASTIC SYSTEMS; TEMPERATURE; TESTING;

ALOPEX ALGORITHM; GLOBAL ERRORS; LOCAL CORRELATIONS; NO ANNEALING; TRAINING ALGORITHMS; TRAINING PROCESS; TRAINING TIME;

ALGORITHMS;

EID: 84950287108     PISSN: 15224899     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SBRN.2000.889726     Document Type: Conference Paper

References (14)

1. E. Ciaccio and E. Tzanakou. The alopex process: Application to real-time reduction of motion artifact. Annual Int. Conf. of IEEE EMBS 12/3, pages 1417-1418, 1990.
2. T. Dasey and E. Micheli-Tzanakou. A pattern recognition application of the alopex process with hexagonal arrays. Int. Joint Conference on Neural Networks, II:119-125, 1990.
3. M. Forcada and R. Carrasco. Learning the initial state of a second-order recurrent neural network during regular-language inference. Neural Computation, 7(5):923-930, 1995.
4. E. Harth and E. Tzanakou. Alopex: A stochastic method for determining visual receptive fields. Vision Research, 14:1475-1482, 1974.
5. R. P. Ñeco and M. L. Forcada. Asynchronous translations with recurrent neural nets. In Proceedings of the International Conference on Neural Networks ICNN'97, volume 4, pages 2535-2540, Houston, Texas, June 1997. June 8-12, 1997.
6. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-408, 1958.
7. E. Tzanakou. What artificial neural networks can do. BMES Bulletin, Biomedical Engineering Society Newsletter, 19(3), 1995.
8. E. Tzanakou. Neural networks in signal and image processing. Proc. ELECTRO'96, pages 337-346, 1996.
9. E. Tzanakou and E. Harth. Determination of visual receptive fields by stochastic methods. Biophys. J., 15:42a, 1973.
10. E. Tzanakou, R. Michalak, and E. Harth. The alopex process: visual receptive fields by response feedback. Biol. Cybern., 35:161-174, 1979.
11. K. Unnikrishnan and K. Venugopal. Learning in connectionist networks using the alopex algorithm. In Proceedings of IJCNN, pages 1926-1931. IEEE Press, 1992.
12. K. Unnikrishnan and K. Venugopal. Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks. Neural Computation, 6(3), May 1994.
13. K. Venugopal, A. Pandya, and R. Sudhakar. Alopex algorithm for adaptive control of dynamical systems. Proc. IJCNN, 11:875-880, 1992.
14. K. Venugopal, A. Pandya, and R. Sudhakar. Invariant recognition of 2d objects using alopex neural networks. In S. Rogers, editor, SPIE Proceedings, Applications of Artificial Neural Networks III, volume 1709, pages 182-190, Orlando, FL, USA, April 1992.