A generalized feedforward neural network architecture for classification and regression

Neural Networks

Volumn 16, Issue 5-6, 2003, Pages 561-568

  Arulampalam, Ganesh ^a   Bouzerdoum, Abdesselam ^a  

^a EDITH COWAN UNIVERSITY   (Australia)

Author keywords

Classification; Feedforward neural network; Generalized shunting neuron; Perceptron; Shunting inhibition

Indexed keywords

MULTILAYER NEURAL NETWORKS; PATTERN RECOGNITION; REGRESSION ANALYSIS;

GENERALIZED SHUNTING NEURON (GSN) MODEL;

FEEDFORWARD NEURAL NETWORKS;

ALGORITHM; ARCHITECTURE; ARTIFICIAL NEURAL NETWORK; COMPUTER ANALYSIS; COMPUTER MODEL; CONFERENCE PAPER; EXPERIMENTAL MODEL; NERVE CELL; PRIORITY JOURNAL; REGRESSION ANALYSIS;

EID: 0038721679     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(03)00116-3     Document Type: Conference Paper

References (33)

1. Arulampalam G., Bouzerdoum A. Training shunting inhibitory artificial neural networks as classifiers. Neural Network World. 10:(3):2000;333-350.
2. Arulampalam G., Bouzerdoum A. Application of shunting inhibitory artificial neural networks to medical diagnosis. Proceedings of the Seventh Australian and New Zealand Intelligent Information Systems Conference (ANZIIS 2001), Perth, Australia. 2001;89-94.
3. Arulampalam G., Bouzerdoum A. Expanding the structure of shunting inhibitory artificial neural network classifiers. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), Honolulu, USA. 2002;2855-2860.
4. Beare R., Bouzerdoum A. Biologically inspired local motion detector architecture. Journal of Optical Society of American Association. 16:(9):1999;2059-2068.
5. Blake, C. L., & Merz, C. J (1998). UCI Repository of machine learning databases. University of California, Department of Information and Computer Science: http://www.ics.uci.edu/~mlearn/MLrepository.html.
6. Bouzerdoum A. Convergence of symmetric shunting competitive neural networks. Green D., Bossomaier T. Complex systems: From biology to computation. 1992;301-312 IOS Press, Amsterdam.
7. Bouzerdoum A. The elementary movement detection mechanism in insect vision. Philosphical Transactions of the Royal Society of London. B-339:1993;375-384.
8. Bouzerdoum A. A new class of high-order neural networks with nonlinear decision boundaries. Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP '99), Perth, Australia. 1999;1004-1009.
9. Bouzerdoum A. Classification and function approximation using feed-forward shunting inhibitory artificial neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), Como, Italy. 2000;613-618.
10. Bouzerdoum A., Pinter R.B. Nonlinear lateral inhibition applied to motion detection in the fly visual system. Pinter R.B., Nabet B. Nonlinear vision. 1992;423-450 CRC Press, Boca Raton.
11. Bouzerdoum A., Pinter R.B. Shunting inhibitory cellular neural networks: derivation and stability analysis. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications. 40:(3):1993;215-221.
12. Carpenter G.A., Grossberg S. The ART of adaptive pattern recognition by a self-organising neural network. IEEE Computer. 21:(3):1988.
13. Cheung H.N., Bouzerdoum A., Newland W. Properties of shunting inhibitory cellular neural networks for colour image enhancement. Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP'99), Perth, Australia. 1999;1219-1223.
14. Demuth H., Beale M. Neural network toolbox user's guide. Version 3 ed:1992;The MathWorks Inc.
15. Duch W., Jankowski N. Transfer functions: hidden possibilities for better neural networks. Proceedings of the Ninth European Symposium on Artificial Neural Networks (ESANN), Brugge. 2001;81-94.
16. Fukushima K., Miyake S., Ito T. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transaction on Systems, Man and Cybernetics. 13:1983;826-834.
17. Furman G.G. Comparison of models for subtractive and shunting lateral-inhibition in receptor-neuron fields. Kybernetik. 2:1965;257-274.
18. Grossberg S. Contour enhancement, short term memory, and constancies in reverberating neural networks. Studies in Applied Mathematics. 52:(3):1973;213-257.
19. Grossberg S. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybernetics. 23:1976;121-134.
20. Grossberg S. Neural networks and natural intelligence. 1988;MIT Press, Cambridge, MA.
21. Hagan M.T., Menhaj M.B. Training feedforward networks with the Marquadt algorithm. IEEE Transactions on Neural Networks. 5:(6):1994;989-993.
22. Hathaway R., Bezdek J.C. Fuzzy c-means clustering of incomplete data. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics. 31:(5):2001;735-744.
23. Haykin S. Neural networks: A comprehensive foundation. 1994;Macmillan, New York.
24. Jankowski N. Flexible transfer functions with ontogenic neural networks. 1999;Computational Intelligence Lab, DCM NCU, Torun, Poland.
25. Jankowski N., Duch W. Optimal transfer function neural networks. Proceedings of the Ninth European Symposium on Artificial Neural Networks (ESANN), Brugge. 2001;101-106.
26. Lettvin J.Y. Res. Quart. Prog. Report. Res. Quart. Prog. Report. 1962;MIT Press, Cambridge, MA.
27. Pinter R.B. Product term nonlinear lateral inhibition enhances visual selectivity for small objects or edges. Journal of Theoretical Biology. 100:1983;525-531.
28. Pinter R.B. Adaptation of receptive field organization by multiplicative lateral inhibition. Journal of Theoretical Biology. 110:1984;435-444.
29. Pinter R.B. Adaptation of spatial modulation transfer function via nonlinear lateral inhibition. Biological Cybernetics. 51:1985;285-291.
30. Prechelt L. PROBEN 1 - A set of neural network benchmark problems and benchmarking rules (Technical Report 21/94). 1994;Fakultat fur Informatik, Universitat Karlsruhe, Karlsruhe, Germany.
31. Sherrah, J (1998). Automatic feature extraction for pattern recognition. PhD Thesis, University of Adelaide, Adelaide.
32. Smieja F.J. Hyperplane spin dynamics, network plasticity and backpropagation learning. 1991;GMD, St Augustin, Germany.
33. Thimm G., Fiesler E. High-order and multilayer perceptron initialization. IEEE Transactions on Neural Networks. 8:(2):1997;349-359.