Critical echo state networks

Márton Albert Hajnal, András Lörincz

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

16 Citations (Scopus)

Abstract

We are interested in the optimization of the recurrent connection structure of Echo State Networks (ESNs), because their topology win strongly influence performance. We study ESN predictive capacity by numerical simulations 011 Mackey-Glass time series, and find that a particular small subset of ESNs is much better than ordinary ESNs provided that the topology of the recurrent feedback connections satisfies certain conditions. We argue that tire small subset separates two large sets of ESNs and this separation can be characterized in terms of phase transitions. With regard to the criticality of this phase transition, we introduce the notion of Critical Echo State Networks (CESN). We discuss why CESNs perform better than other ESNs.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Pages658-667
Number of pages10
ISBN (Print)3540386254, 9783540386254
DOIs
Publication statusPublished - Jan 1 2006
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: Sep 10 2006Sep 14 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4131 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Neural Networks, ICANN 2006
CountryGreece
CityAthens
Period9/10/069/14/06

Keywords

  • Critical point
  • Echo state network
  • Phase transition
  • Prediction
  • Time series

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hajnal, M. A., & Lörincz, A. (2006). Critical echo state networks. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings (pp. 658-667). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4131 LNCS - I). Springer Verlag. https://doi.org/10.1007/11840817_69