Adaptive interaction and its application to neural networks

Information sciences

Volumn 121, Issue 3, 1999, Pages 201-215

  Brandt, Robert D ^a   Lin, Feng ^b  

^a Intelligent Devices   (United States)

^b Wayne State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; ADAPTIVE SYSTEMS; BACKPROPAGATION; COMPUTER SIMULATION; ERROR ANALYSIS; INTERACTIVE COMPUTER SYSTEMS;

ADAPTIVE INTERACTION;

NEURAL NETWORKS;

EID: 0033357978     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(99)00090-0     Document Type: Article

References (30)

1. W.R. Ashby, Design for a Brain, Wiley, New York, 1960.
2. R.C. Bolles, M.D. Beecher, (Eds.) Evolution and Learning, Lawrence Erlbaum, London, 1988.
3. R.D. Brandt, F. Lin, Supervised learning in neural networks without explicit error back-propagation, in: Proceedings of the 32nd Annual Allerton Conference on Communication, Control and Computing, 1994, pp. 294-303.
4. R.D. Brandt, F. Lin, Can supervised learning be achieved without explicit error back-propagation? in: Proceedings of the International Conference on Neural Networks, 1996a, pp. 300-305.
5. R.D. Brandt, F. Lin, Optimal layering of neurons, in: 1996 IEEE International Symposium on Intelligent Control, 1996b, pp. 497-501.
6. R.D. Brandt, F. Lin, Supervised learning in neural networks without feedback network, in: 1996 IEEE International Symposium on Intelligent Control, 1996c, pp. 86-90.
7. R.D. Brandt, F. Lin, Theory of Adaptive Interaction, AFI Press (to appear).
8. Crick F. The recent excitement about neural networks. Nature. 337:1989;129-132.
9. Bolenko B.K., Card H.C. Tolerance to analog hardware of on-chip learning in back-propagation networks. IEEE Trans. Neural Networks. 6(5):1995;1045-1052.
10. S. Haykin, Neural Networks: A Comprehensive Foundation, IEEE Press, New York, 1994.
11. J. Hertz, A. Krogh, R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA, 1991.
12. J.H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, 1992.
13. Hollis P.W., Paulos J.J. A neural network learning algorithm tailored for VLSI implementation. IEEE Trans. Neural Networks. 5(5):1994;781-791.
14. P.A. Ioannou, J. Sun, Robust Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, 1996.
15. R. Isermann, K.-H. Lachmann, D. Matko, Adaptive Control Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992.
16. Y. D. Landau, Adaptive Control: The Model Reference Approach, Marcel Dekker, New York, 1979.
17. Lansner J.A., Lehmann T. An analog CMOS chip set for neural networks with arbitrary topologies. IEEE Trans. Neural Networks. 4(3):1993;441-444.
18. F. Lin, R.D. Brandt, G. Sailalis, Self-tuning of PID controllers by adaptive interaction, 1998 (preprint).
19. F. Lin, R.D. Brandt, G. Sailalis, Parameter estimation using adaptive interaction, 1998 (preprint).
20. F. Lin, R.D. Brandt, Adaptive interaction: A new approach to adaptation, 1998 (preprint).
21. Linares-Barranco B., Sanchez-Sinencio E., Rodriguez-Vazques A., Huertas J.L. A CMOS adaptive BAM with on-chip learning and weight refreshing. IEEE Trans. Neural Networks. 4(3):1993;445-455.
22. D.G. Luenberger, Optimization by Vector Space Methods, Wiley, New York, 1968.
23. D.B. Parker, Optimal algorithms for adaptive networks: second-order back-propagation, second-order direct propagation, and second-order Hebbian learning, in: Proceedings of the IEEE International Conference on Neural Networks, 1987, pp. 593-600.
24. K.H. Pribram, Rethinking Neural Networks: Quantum Fields and Biological Data, Lawrence Erlbaum Associates, Publishers 1993.
25. D.E. Rumelhart, G.E. Hinton, G.E. Williams, Learning internal representations by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, Foundations MIT Press, Cambridge, 1986, pp. 318-362.
26. G.C. Williams, Adaptation and Natural Selection, Princeton University Press, Princeton, 1966.
27. R.J. Williams, On the use of back-propagation in associative reinforcement learning, in: Proceedings of the IEEE International Conference on Neural Networks, 1988, pp. 263-270.
28. R.J. Williams, Towards a theory of reinforcement-learning connectionist systems, Technical Report, NU-CCS-88-3, Northeastern University, 1988.
29. Wu C.-Y., Lan J.-F. CMOS current-mode neural associative memory design with on-chop learning. IEEE Trans. Neural Networks. 7(1):1996;167-177.
30. D. Zipser, D.E. Rumelhart, Neurobiol. significance of new learning models, in: E. Schwartz (Eds.), Computational Neuroscience, MIT Press, Cambridge, 1990, pp. 192-200.