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Adaptive models in neural networks

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

Artificial neural networks (ANNs) are principally attractive for their high degree of parallelism, for their associative memory properties, and for their ability to swiftly compute “near-optimal” solutions to highly constrained optimization problems. In this paper we examine the essential adaptive models that have been proposed for ANNs.

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References

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Author information

Authors and Affiliations

  1. Cybernetics Research Lab., Electrical Eng. Dept., University of Maryland, 20742, College Park, MD

    Panos A. Ligomenides

Authors
  1. Panos A. Ligomenides
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Editor information

José Mira Joan Cabestany Alberto Prieto

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© 1993 Springer-Verlag Berlin Heidelberg

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Ligomenides, P.A. (1993). Adaptive models in neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_146

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  • DOI: https://doi.org/10.1007/3-540-56798-4_146

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

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