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Local Modeling Using Self-Organizing Maps and Single Layer Neural Networks

  • Oscar Fontenla-Romero
  • Amparo Alonso-Betanzos
  • Enrique Castillo
  • Jose C. Principe
  • Bertha Guijarro-Berdiñas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)

Abstract

The paper presents a method for time series prediction using local dynamic modeling. After embedding the input data in a reconstruction space using a memory structure, a self-organizing map (SOM) derives a set of local models from these data. Afterwards, a set of single layer neural networks, trained optimally with a system of linear equations, is applied at the SOM’s output. The goal of the last network is to fit a local model from the winning neuron and a set of neighbours of the SOM map. Finally, the performance of the proposed method was validated using two chaotic time series.

Keywords

Normalise Mean Square Error Time Series Prediction Chaotic Time Series Desired Output Train Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Oscar Fontenla-Romero
    • 1
  • Amparo Alonso-Betanzos
    • 1
  • Enrique Castillo
    • 2
  • Jose C. Principe
    • 3
  • Bertha Guijarro-Berdiñas
    • 1
  1. 1.Laboratory for Research and Development in Artificial Intelligence, Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Department of Applied Mathematics and Computer SciencesUniversity of CantabriaSantanderSpain
  3. 3.Computational NeuroEngineering Laboratory, Electrical and Computer Engineering DepartmentUniversity of FloridaGainesvilleUSA

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