Connection topologies for combining genetic and least square methods for neural learning

Journal of Intelligent Systems

Volumn 13, Issue 3, 2004, Pages 199-232

  Ghosh, Ranadhir ^a  

^a UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Evolutionary learning algorithms; Learning algorithms; Neural network architecture

Indexed keywords

BACKPROPAGATION; COMPUTATIONAL COMPLEXITY; ERROR ANALYSIS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILITY;

DATASETS; EVOLUTIONARY LEARNING ALGORITHMS; LEAST SQUARE METHODS; NEURAL NETWORK ARCHITECTURES;

NEURAL NETWORKS;

EID: 9144257550     PISSN: 03341860     EISSN: None     Source Type: Journal    
DOI: 10.1515/JISYS.2004.13.3.199     Document Type: Article

References (28)

1. Anderson, J.A. 1972. A simple neural network generating an interactive memory, Mathematical Biosciences, 14, 197-220.
2. Abraham, A. 2001. Neuro-Fuzzy Systems: state-of-the-art modeling techniques, Connectionist Models of Neurons, Learning Process, and Artificial Intelligence, edited by Jose Mira and Alberto Prieto Springer-Verlag Germany, LNCS 2084, 269-276.
3. th International Conference on Cognitive and Neural Systems, Boston, Massachusetts, USA, Boston University Press.
4. st International Workshop on Hybrid Intelligent Systems, 79-92.
5. Brent, R.P. 1991. Fast training algorithms for multilayer neural nets, IEEE Transactions on Neural Networks, 2, 346-354.
6. Doering, A., Galicki, M. and Witte, H. 1997. Structure optimization of neural networks with the A* algorithm, IEEE Transactions on Neural Networks, 8, 307-317.
7. Gutierrez, G., Isasi, P., Molina, J.M., Sanchis, A. and Galvan, I. M. 2001. Evolutionary cellular configurations for designing feedforward neural network architectures, Proceedings of the International Work-Conference on Artificial and Natural Neural Networks, 514-521.
8. Ghosh, R. and Verma, B. 2001. Least square method based evolutionary neural learning algorithm, Proceedings of the IEEE International Joint Conference on Neural Networks, 2596-2601.
9. Ghosh, R. and Verma, B. 2003. Finding architecture and weights for ANN using evolutionary based least square algorithm, International Journal on Neural Systems, 13, 13-24.
10. Hush, D. R. and Home, B. G. 1993. Progress in supervised neural networks, IEEE Signal Processing Magazine, 10, 8-39.
11. Holland, J. H. 1975. Adaptation in natural and artificial systems, Ann Arbor, Michigan, USA: The University of Michigan Press.
12. Koeppen, M., Teunis, M. and Nickolay, B. 1994. Neural network that uses evolutionary learning Proceeding of the IEEE International Conference on Neural Networks, 5, 635-639.
13. Kohonen, T. 1982. Self-Organization formation of topologically correct feature maps, Biological Cybernetics, 43, 59-69.
14. th IEEE International Conference on Tools with Artificial Intelligence, 32-36.
15. th International Joint Conference on Artificial Intelligence IJCAI-89, 1, 762-767.
16. Moller, M.F. 1993. A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 525-523.
17. Omatu, S. and Deris, S. 1996. Stabilization of inverted pendulum by the genetic algorithm, Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, ETFA'96, 1, 282-287.
18. Omatu, S. and Yoshioka, M. 1997. Self tuning neuro PID control and applications, Proceedings of the IEEE International Conference on Systems, Man, and Cybernatics, 3, 1985-1989.
19. Petridis, V. Kazarlis, S. Papaikonomu A. and Filelis, A. 1992. A hybrid genetic algorithm for training neural network, Artificial Neural Networks, 2, 953-956.
20. Richard M.D. and Lippmann, R.P. 1991. Neural network classifiers estimate bayesian a posterior probabilities, Neural Computation, 3, 461-483.
21. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. 1986. Learning internal representations by error propagation, Parallel Distributed Processing, edited by Rumelhart, D.E. and McClelland, J.L., Cambridge, Massachusetts, USA, MIT Press, 1, 318-362.
22. Rechenberg, I. 1965. Cybernatic solution path of an experimental problem, Royal Aircraft Establishment, Library Translation no. 1122, Farnborough, Hants, U.K.
23. Schiffmann, W., Joost, M. and Werner R. 1993. Comparison of optimized backpropagation algorithms, Proceedings of The European Symposium on Artificial Neural Networks, 97-104.
24. Topchy, A.P. and Lebedko, O.A. 1997. Neural network training by means of cooperative evolutionary search, Nuclear Instruments & Methods in Physics Research, Section A: Accelerators, Spectometers, Detectors and Associated Equipment, 389, 240-241.
25. Williams, R.J. and Zipset, D. 1989. A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1, 279-280.
26. Willshaw, D.J. and Malsburg, V.D. 1976. How patterned neural connections can be set up by self-organization, Royal Society London, B194, 431-445.
27. Whitley, D., Starkweather, T. and Bogart, C. 1990. Genetic algorithms and neural networks - optimizing connections and connectivity, Parallel Computing, 14, 347-361.
28. Werbos, P.J. 1974. Beyond regression: New tools for prediction and analysis in the behavioural sciences, PhD dissertation, Cambridge, Massachusetts, USA: Harvard University.