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Hybrid Intelligent Systems pp 385–402Cite as

Time Series Forecasting of Tomato Prices and Processing in Parallel in Mexico Using Modular Neural Networks

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 208))

Abstract

In this paper we describe the concepts of Time Series, Neural Networks, Modular Neural Networks, and Parallelism. Modular Neural Networks and Parallel Processing for Time Series Forecasting of Tomato Prices in Mexico are described in this paper. A particular modular neural network architecture implemented in parallel was used. Simulation results with the modular neural network approach for this application are very good.

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Author information

Authors and Affiliations

  1. Division of Graduate Studies, Tijuana Institute of Technology, Mexico

    Ileana Leal & Patricia Melin

Authors
  1. Ileana Leal
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  2. Patricia Melin
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Department of Computer Science, Tijuana Institute of Technology, 4207, Chula Vista, CA, 91909, USA

    Patricia Melin

  3. Polish Academy of Sciences, Systems Research Institute, Newelska 6, Warszawa, 01-447, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6J 2V4, Canada

    Witold Pedrycz

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Leal, I., Melin, P. (2007). Time Series Forecasting of Tomato Prices and Processing in Parallel in Mexico Using Modular Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37421-3_23

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  • DOI: https://doi.org/10.1007/978-3-540-37421-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37419-0

  • Online ISBN: 978-3-540-37421-3

  • eBook Packages: EngineeringEngineering (R0)

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