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Intelligent Signal Processing

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Advances in Signal Processing for Nondestructive Evaluation of Materials

Part of the book series: NATO ASI Series ((NSSE,volume 262))

Abstract

For the past 40 years or so, interest in the use of (artificial neural networks has been motivated by the recognition that the human brain operates in a manner that is entirely different from a conventional digital computer. A neural network is made up of an interconnection of a large number of nonlinear computation units known as neurons, which operate in a highly parallel fashion. Interest in the use of neural networks was reignited in the 1980s largely due to (1) the popularization of the back-propagation algorithm as a tool for the training of multilayer perceptrons, and (2) the use of attractor neural networks (exemplified by the Hopfield model) as content-addressable memories and optimization networks. For a historical account of neural networks, the reader is referred to Cowan (1990) and Haykin (1994).

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References

  • Broomhead, D.S., and D. Lowe, 1988. “Multivariable functional interpolation and adaptive networks”, Complex Systems, vol. 2, pp. 321–355.

    Google Scholar 

  • Cohen, L., 1989. “Time-frequency distributions — A review”, Proc. IEEE, vol. 77, pp. 941–981.

    Article  Google Scholar 

  • Cowan, J.D., 1990. “Neural networks: The early days”, In Advances in Neural Information Processing Systems, 2, ( D.S. Touretzky, ed.), pp. 828–842, San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Daubechies, I., 1990. “The wavelet transform, time-frequency localization and signal analysis”, IEEE Trans. Information Theory, vol. 36, pp. 961–1065.

    Article  Google Scholar 

  • Haykin, S., 1994. Neural Networks: A Comprehensive Foundation, New York: Macmillan.

    Google Scholar 

  • Haykin, S. and T.H. Bhattacharya, 1992. “Adaptive radar detection using supervised learning networks”, Computational Neuroscience Symposium, pp. 35–51.

    Google Scholar 

  • Hlawatsch, F. and G.F. Bourdeaux-Bartels, 1992. “Linear and quadratic timefrequency signal representations”, IEEE Signal Processing Magazine, vol. 9 (2), pp. 21–68.

    Article  Google Scholar 

  • Jacobs, R.A. and M.I. Jordan, 1991. “A competitive modular connectionist architecture”. In Advances in Neural Information Processing Systems 3 ( R.P. Lippmann, J.E. Mood, and D.J. Touretzky, eds.), pp. 767–773, San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Jordan, M.I. and R.A. Jacobs, 1992. “Hierarchies of adaptive experts”, In Advances in Neural Information Processing Systems 4 ( J.E. Moody, S.J. Hanson, and R.P. Hippmann, eds.), pp. 985–992, San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Kung, S.Y. and K.I. Diamantaras, 1991. “A neural network learning algorithm for adaptive principal component extraction ( APEX)”, ICASSP, pp. 861–864.

    Google Scholar 

  • Poggio, T. and F. Girosi, 1990. “Networks for approximation and learning”, Proc. IEEE, vol. 78, pp. 1481–1497.

    Article  Google Scholar 

  • Riley, M.D., 1989. Speech Time-Frequency Representations, Boston, MA: Kluwer.

    Google Scholar 

  • Rioul, O. and M. Vetterli, 1991. “Wavelets and signal processing”, IEEE Signal Processing Magazine, vol. 8 (4), pp. 14–38.

    Article  Google Scholar 

  • Rumelhart, D.E., and J.L. McClelland, 1986. Parallel Distributed Processing, Vol. 1, Cambridge, MA: MIT Press.

    Google Scholar 

  • Sanger, T.D., 1989. “Optimal unsupervised learning in a single-layer linear feedforward neural network”, Neural Networks, vol. 12, pp. 459–473.

    Article  Google Scholar 

  • Vapnik, V.N., 1992. “Principles of risk minimization for learning theory”, In Advances in Neural Information Processing Systems 4 ( J.E. Moody, S.J. Hanson, and R.P. Lippmann, eds.), pp. 831–838, San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Zadeh, L.A., 1993, Plenary Lecture, IEEE International Conference on Neural Networks, San Francisco, CA.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Communications Research Laboratory, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1

    Simon Haykin

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  1. Simon Haykin
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Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Université Laval, Québec City, Québec, Canada

    X. P. V. Maldague

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© 1994 Springer Science+Business Media Dordrecht

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Haykin, S. (1994). Intelligent Signal Processing. In: Maldague, X.P.V. (eds) Advances in Signal Processing for Nondestructive Evaluation of Materials. NATO ASI Series, vol 262. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1056-3_1

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  • DOI: https://doi.org/10.1007/978-94-011-1056-3_1

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4459-2

  • Online ISBN: 978-94-011-1056-3

  • eBook Packages: Springer Book Archive

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