The adaptive learning rates of extended Kalman filter based training algorithm for wavelet neural networks

Joo Kim Kyoung, Bae Park Jin, Ho Choi Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMICAI 2006
Subtitle of host publicationAdvances in Artificial Intelligence - 5th Mexican International Conference on Artificial Intelligence, Proceedings
Pages327-337
Number of pages11
Publication statusPublished - 2006
Event5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence - Apizaco, Mexico
Duration: 2006 Nov 132006 Nov 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4293 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence
CountryMexico
CityApizaco
Period06/11/1306/11/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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