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Anti boundary effect wavelet decomposition echo state networks

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a novel approach based on wavelet decomposition and echo state networks to discover the multiscale dynamics of time series which we call anti-boundary-effect wavelet decomposition and echo state networks (ABE-WESNs). ABE-WESNs use the wavelet decomposition as preprocessing steps and choose a matched ESNs for every scale level. We use the data extension methods to overcome the boundary effect. The introduced weight factors can both resolve the problem of cumulation of errors resulting from the wavelet decomposition. Experiments and engineering applications show that the ABE-WESNs can accurately model and predict some time series with multiscale properties.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages445-454
Number of pages10
EditionPART 1
DOIs
StatePublished - 2011
Event8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China
Duration: 29 May 20111 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Symposium on Neural Networks, ISNN 2011
Country/TerritoryChina
CityGuilin
Period29/05/111/06/11

Keywords

  • Boundary Effect
  • Echo State Network
  • Time Series Prediction
  • Wavelet

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