Dynamical process of learning chaotic time series by neural networks

Tsuyoshi Hondou, Yasuji Sawada

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

Abstract

We report the result of computer simulations on the learning process of temporal series by artificial neural networks. In our simulation, we used a feedforward neural network model with 4-layers to study the capability and dynamical learning process of chaotic time series produced by triangular (tent) maps. We found a critical time (tcr) at which learning process proceeds abruptly. We also found that the critical time (tcr) is shorter, the larger is the initial deviation from the target of learning. We tried detailed discussion about the learning process to explain these interesting phenomena, and new order parameter coherency is introduced to characterize these processes.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages2387-2390
Number of pages4
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993 Dec 1
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume3

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Hondou, T., & Sawada, Y. (1993). Dynamical process of learning chaotic time series by neural networks. In Proceedings of the International Joint Conference on Neural Networks (pp. 2387-2390). (Proceedings of the International Joint Conference on Neural Networks; Vol. 3). Publ by IEEE.