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International Conference on Artificial Neural Networks

ICANN 1997: Artificial Neural Networks — ICANN'97 pp 547–552Cite as

A perceptron-based approach to piecewise linear modeling with an application to time series

  • Part IV: Signal Processing: Blind Source Separation, Vector Quantization, and Self-Organization
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Abstract

Piecewise linear models are attractive when modeling a wide range of nonlinear phenomena but determining simultaneously the domain decomposition and the corresponding parameter values is a challenging problem. We show that this problem can be formulated as that of partitioning an inconsistent linear system into a minimum number of consistent subsystems and we describe a greedy algorithm, based on a simple variant of the perceptron procedure, which provides good approximate solutions in a short amount of time. The general approach is applied to piecewise linear modeling of time series.

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Author information

Authors and Affiliations

  1. School of OR and Theory Center, Cornell University, 14853, Ithaca, NY, USA

    Edoardo Amaldi

  2. Signal Processing Laboratory, EPFL, CH-1015, Lausanne, Switzerland

    Marco Mattavelli & Jean-Marc Vesin

Authors
  1. Marco Mattavelli
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  2. Edoardo Amaldi
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  3. Jean-Marc Vesin
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Mattavelli, M., Amaldi, E., Vesin, JM. (1997). A perceptron-based approach to piecewise linear modeling with an application to time series. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020211

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  • DOI: https://doi.org/10.1007/BFb0020211

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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