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International School on Neural Networks, Initiated by IIASS and EMFCSC

NN 2004: Nonlinear Speech Modeling and Applications pp 43–53Cite as

Signal Processing in a Nonlinear, NonGaussian, and Nonstationary World

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3445))

Abstract

This article discusses three specific issues of particular interest in the study of nonlinear dynamical systems:

  • Bayesian estimation, exemplified by particle filtering;

  • Learning in recurrent neural networks;

  • Correlative learning, exemplified by the ALOPEX algorithm.

By and large, the discussion is of a philosophical nature.

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Author information

Authors and Affiliations

  1. McMaster University, Hamilton, Ontario, Canada, L8S 4K1

    Simon Haykin

Authors
  1. Simon Haykin
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Editor information

Editors and Affiliations

  1. CNRS LTCI/TSI Paris, 46 rue Barrault, 75634, Paris Cedex 13, France

    Gérard Chollet

  2. Department of Psychology, Second University of Naples, and IIASS, Via Pellegrino 19, 84019, Vietri sul Mare, SA, Italy

    Anna Esposito

  3. Escola Universitària Politècnica de Mataró, Universitat Politècnica de Catalunya, Barcelona, Spain

    Marcos Faundez-Zanuy

  4. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, SA, Italy

    Maria Marinaro

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Haykin, S. (2005). Signal Processing in a Nonlinear, NonGaussian, and Nonstationary World. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_3

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  • DOI: https://doi.org/10.1007/11520153_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27441-4

  • Online ISBN: 978-3-540-31886-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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