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The Adaptive Learning Rates of Extended Kalman Filter Based Training Algorithm for Wavelet Neural Networks

  • Kyoung Joo Kim
  • Jin Bae Park
  • Yoon Ho Choi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

Since the convergence of neural networks depends on learning rates, the learning rates of training algorithm for neural networks are very important factors. Therefore, we propose the Adaptive Learning Rates(ALRs) of Extended Kalman Filter(EKF) based training algorithm for wavelet neural networks(WNNs). The ALRs of the EFK based training algorithm produce the convergence of the WNN. Also we derive the convergence analysis of the learning process from the discrete Lyapunov stability theorem. Several simulation results show that the EKF based WNN with ALRs adapt to abrupt change and high nonlinearity with satisfactory performance.

Keywords

Extend Kalman Filter Asymptotic Convergence Wavelet Neural Network Past Output Adaptive Learn Rate 
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

  • Kyoung Joo Kim
    • 1
  • Jin Bae Park
    • 1
  • Yoon Ho Choi
    • 2
  1. 1.Yonsei UniversitySeoulKorea
  2. 2.Kyonggi UniversitySuwon, Kyonggi-DoKorea

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