Dynamic neural networks and optimal signal processing

Handbook of Neural Network Signal Processing

Volumn , Issue , 2001, Pages 6-1-6-28

  Principe, Jose C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 23544462684     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (70)

1. Wiener, N., Extrapolation, Interpolation and Smoothing of Stationary Time Series, with Engineering Applications, JohnWiley & Sons, New York, 1949.
2. Widrow, B. and Stearns, S.D. Adaptive Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1985.
3. Behnke, H., Bachmann, F., Fladt, K., and Suss, W., Fundamentals of Mathematics, Vol. III, MIT Press, Cambridge, MA, 1983.
4. Szego, G., Orthogonal polynomials, Vol. XXII, American Mathematical Society Colloquium, 1939.
5. Whaba, G., Splines for observational data, SIAM, 1990.
6. Rao, C., Linear Statistical Inference and its Applications, JohnWiley & Sons, New York, 1965.
7. Kolmogorov, A., Sur l’interpolation et extrapolation des suites stationaires, Compte rendus de l’Acad. Sci., Vol. 208, 2043-2045, 1939.
8. Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
9. Glivenko, V., Sulla determinazione empirica di probabilita, G. Inst. Ital. Attuari, 4, 1933.
10. Principe, J., Euliano, N., and Lefebvre, C., Neural Systems: Fundamentals through Simulations, CDROM textbook, JohnWiley & Sons, New York, 2000.
11. Bishop, C., Neural Networks for Pattern Recognition, Oxford Press, Oxford, UK, 1995.
12. Hornik, K., Stinchcombe, M., and White, H., MLPs are universal approximators, Neural Networks, 2, 359-366, 1989.
13. Park, J. and Sandberg, E., Universal approximation using radial basis function networks, Neural Computation, 3, 303-314, 1989.
14. Barron, A., Universal approximation bounds for superposition of sigmoid functions, IEEE Transactions on Information Theory, 39(3), 930-945, 1993.
15. Poggio, T. and Girosi, F., Networks for approximation and learning, Proceedings of the IEEE, 78, 1990.
16. Moody, J. and Darken, C., Fast learning in networks of locally tuned processing units, Neural Computation, 1, 281-294, 1989.
17. Hertz, J., Krogh, A., and Palmer, R., Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA, 1991.
18. Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan College Publishing, New York, 1994.
19. Box, G.E.P. and Jenkins, G.M., Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
20. Kailath, T., A view of three decades of linear filtering theory, IEEE Transactions on Information Theory, IT-20, 146-181, 1974.
21. Casdagli, M., Nonlinear prediction of chaotic time series, Physics D 35, 335, 1989.
22. Wan, E., Time series prediction by using a connectionist network with internal delay lines, in Times Series Prediction: Forecasting the Future and Understanding thePast, (Eds. Weigendand Gerschenfeld), Addison-Wesley, Reading, MA, 1994, 195-217.
23. Mozer, M., Neural architectures for temporal sequence processing, in Predicting the Future and Understanding the Past, (Eds. Weigend and Gerschenfeld), Addison-Wesley, Reading, MA, 1994.
24. Principe, J.C., Wang, L., and Kuo, J.M., Chaotic time series modeling with neural networks, Signal Analysis and Prediction, (Eds. Prochazka et al.), Birkhauser, 1998, 275-289.
25. Packard, N.H., Crutchfield, J.P., Farmer, J.D., and Shaw, R.S., Geometry from a time series, Physics Review Letters, 45, 712, 1980.
26. Takens, F., Detecting strange attractors in turbulence, in Dynamical Systems and Turbulence, (Eds.D.A. Rand and L.S. Yang), Springer-Verlag, Berlin, 1981, 365-381.
27. Kuo, J.M. and Principe, J., Noise reduction instate space using the focused gammamodel, Proceedings ICASSP94, 2, 533-536, 1994.
28. Sauer, T., Yorke, J.A., and Casdagli, M., Embedology, Journal of Statistical Physics, 65, (3/4), 579-616, 1991.
29. Grassberger, P. and Procaccia, I., Measuring the strangeness of strange attractors, Physica 9D, 189-208, 1983.
30. Abarbanel, H.D.I., Brown, R., Sidorowich, J.J., and Tsimring, L.S., The analysis of observed chaotic data in physical systems, Review of Modern Physics, 65(4), 1331, 1993.
31. Principe, J., Wang, L., and Motter, M., Local dynamic modeling with self-organizing feature maps and applications to nonlinear system identification and control, Special Issue on Intelligent Signal Processing, IEEE Proceedings, 86(11), 2240-2258, 1998.
32. Haykin, S. and Principe, J., Dynamic modeling of chaotic time series with neural networks, IEEE DSP Magazine, May, 1998.
33. Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., and Lang, K., Phoneme recognition using timedelay neural networks, IEEE Transactions of the ACSSP, 37(3), 328-339, 1989.
34. Narendra, K.S. and Mukhopadhyay, S., Adaptive control using neural networks and approximate models, in IEEE Transactions on Neural Networks, 8(3), 475-485, 1997.
35. Lapedes, A. and Farber, R., Nonlinear signal processing using neural networks: prediction and system modeling, Tech. Rep. LA-UR-87-2662, Los Alamos Natl. Lab., Los Alamos, NM, 1987.
36. Singer, A.C., Wornell, G., and Oppenheim, A., Codebook prediction: a nonlinear signal modeling paradigm, IEEE ICASSP, 5, 325, 1992.
37. Volterra, V., Theory of Functionals and of Integral and Integro-Differential Equations, Dover, New York, 1959.
38. Rugh, W., Nonlinear System Theory: The Volterra/Wiener Approach, John Hopkins University Press, Baltimore, MD, 1981.
39. Wiener, N., Nonlinear problems in random theory, MIT RLE Tech Report #355, 1959.
40. Figueiredo, R. and Dwyer, T., A best approximation framework and implementation for simulation of large scale nonlinear systems, IEEE Transactions on Circuits and Systems, CAS-27:11, 1005-1014, 1980.
41. Mhaskar, N., Approximation properties for a multilayer feedforward artificial neural network, Ad. Comp. Math., 1, 61-80, 1993.
42. Boyd, S. and Chua, L, Fading memory and the problem of approximating nonlinear operators with Volterra series, IEEE Transactions on Circuits and Systems, CAS:11, 1150-1161, 1985.
43. Boyd, S., Volterra Series: Engineering Fundamentals, Ph.D. dissertation, University of California, Berkeley, 1985.
44. Sandberg, I., Structure theorems for nonlinear systems, Multidimensional Systems and Signal Processing, 2, 267-286, 1991.
45. Sandberg, I. and Xu, L., Uniform approximation of multidimensional myopic maps, IEEE Transactions on Circuits and Systems, 44, 477-485, 1997a.
46. Stone, M., A generalized weierstrass approximation theorem, in Studies of Modern Analysis, Vol. 1, Prentice-Hall, Englewood Cliffs, NJ, 1962.
47. De Vries, B. and Principe, J.C., The gamma model - a new neuralmodel for temporal processing, Neural Networks, 5(4), 565-576, 1992.
48. Tsoi, A. and Back, A., Locally recurrent globally feedforward networks: a critical review of architectures, IEEE Transactions on Neural Networks, 5(2), 229-239, 1994.
49. Middleton, R. and Goodwin, G., Digital Estimation an Control: A Unified Approach, Prentice-Hall, Englewood Cliffs, NJ, 1990.
50. Kautz, W., Transient synthesis in the time domain, IRE Transactions on Circuit Theory, 1, 29-39, 1954.
51. Churchill, R., Introduction to Complex Variables and Applications, McGraw-Hill, New York, 1948.
52. Itakura, F. and Saito, S., Digital filtering techniques for speech analysis and synthesis, Proceedings of the 7th International Congress on Acoustics, Budapest, 1971.
53. Haykin, S., Adaptive Filter Theory, Prentice-Hall, Englewood Cliffs, NJ, 1991.
54. Goodwin, G.C. and Sin, K.S., Adaptive Filtering, Prediction and Control, Prentice-Hall, Englewood Cliffs, NJ, 1984.
55. Sandberg, I., and Xu, L., Uniformapproximation and gamma neural networks, Neural Networks, 10, 781-784, 1997b.
56. Principe, J.C. and Kuo, J.M., Dynamic modeling of chaotic time series with neural networks, Proceedings of Neural Information Processing Systems, 7, 311-318, 1995.
57. Principe, J., deVries, B., and Guedes de Oliveira, P., The gamma filters: a new class of adaptive IIR filters with restricted feedback, IEEE Transactions on Signal Processing, 41(2), 649-656, 1993.
58. Principe, J., Kuo, J.M., and Celebi, S., An analysis of short termmemory structures in dynamic neural networks, in IEEE Transactions on Neural Networks, special issue on dynamic nets, 5(2), 331-337, 1995.
59. Principe, J. and Tracey, J., Isolated word speech recognition using the gamma model, Journal of Artificial Neural Networks, 1(14), 481-489, 1994.
60. Papoulis, A., Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New York, 1984.
61. Oliveira e Silva, T., On the determination of the optimal pole position of Laguerre filters, IEEE Transactions on Signal Processing, 43(9), 2079-2087, 1995.
62. Wahlberg, B., System identification using Laguerre models, IEEE Transactions on Automatic Control, 36(5), 551-562, 1991.
63. Oliveira e Silva, T., Optimality conditions for truncated Kautz networks with two periodically repeating complex conjugate poles, IEEE Transactions on Automatic Control, 40(2), 342-346, 1995.
64. Makhoul, J., Stable and efficient latticemethods for linear prediction, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-25, 423-428, 1977.
65. Principe, J., Celebi, S., deVries, B., and Harris, J., Locally recurrent networks: the gamma operator, properties and extensions, in Neural Networks and Pattern Recognition, (Eds. Omidvar and Dayhoff), Academic Press, New York, 1996, 311-344.
66. Lee, Y., Statistical Theory of Communication, JohnWiley & Sons, New York, 1960.
67. Celebi, S. and Principe, J., Parametric least squares approximation using gamma bases, IEEE Transactions on Signal Processing, 43(3), 781-784, 1995.
68. Feldkamp, L. and Puskorius, G., A signal processing framework based on dynamic neural networks with applications to the problems in adaptation, filtering, and classification, Proceedings of the IEEE, 86, 2259-2277, 1998.
69. Bengio, Y., Simard, P., and Fransconi, P., Learning long term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, 5, 157-166, 1994.
70. Euliano, N. and Principe, J., Dynamic subgrouping in RTRLpr ovides a faster O(N2) algorithm, Proceedings of ICASSP, 2000.