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Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System

  • Kai Zhang
  • Gen-Zhi Guan
  • Fang-Fang Chen
  • Lin Zhang
  • Zhi-Ye Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.

Keywords

Convergent Speed Small Positive Real Number Predictive Control System Competition Layer Multiple Local Model 
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 2006

Authors and Affiliations

  • Kai Zhang
    • 1
  • Gen-Zhi Guan
    • 1
  • Fang-Fang Chen
    • 2
  • Lin Zhang
    • 1
  • Zhi-Ye Du
    • 1
  1. 1.School of Electrical EngineeringWuhan UniversityWuhanChina
  2. 2.College of FinanceHunan UniversityChangshaChina

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