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Evolutionary neural networks for nonlinear dynamics modeling

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the Breeder Genetic Algorithms are considered to provide the optimal set of synaptic weights of the network. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with Genetic Algorithms and Back Propagation learning technique is performed.

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Authors and Affiliations

  1. Research Institute on Parallel Information Systems (IRSIP), National Research Council of Italy (CNR), Via P. Castellino, 111, 80131, Naples, Italy

    I. De Falco, A. Iazzetta, P. Natale & E. Tarantino

Authors
  1. I. De Falco
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  2. A. Iazzetta
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  3. P. Natale
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  4. E. Tarantino
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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De Falco, I., Iazzetta, A., Natale, P., Tarantino, E. (1998). Evolutionary neural networks for nonlinear dynamics modeling. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056901

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  • DOI: https://doi.org/10.1007/BFb0056901

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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