Effect of time correlation of input patterns on the convergence of on-line learning

Tsuyoshi Hondou; Mitsuaki Yamamoto; Yasuji Sawada; Yoshihiro Hayakawa

doi:10.1103/PhysRevE.53.4217

Effect of time correlation of input patterns on the convergence of on-line learning

Tsuyoshi Hondou, Mitsuaki Yamamoto, Yasuji Sawada, Yoshihiro Hayakawa

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

We studied the effects of time correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with the backpropagation algorithm. By using a chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with the learning parameter emerges when time-correlated patterns accelerate the learning process.

Original language	English
Pages (from-to)	4217-4220
Number of pages	4
Journal	Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume	53
Issue number	4
DOIs	https://doi.org/10.1103/PhysRevE.53.4217
Publication status	Published - 1996
Externally published	Yes

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Statistics and Probability
Condensed Matter Physics

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10.1103/PhysRevE.53.4217

Cite this

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abstract = "We studied the effects of time correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with the backpropagation algorithm. By using a chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with the learning parameter emerges when time-correlated patterns accelerate the learning process.",

author = "Tsuyoshi Hondou and Mitsuaki Yamamoto and Yasuji Sawada and Yoshihiro Hayakawa",

year = "1996",

doi = "10.1103/PhysRevE.53.4217",

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AU - Sawada, Yasuji

AU - Hayakawa, Yoshihiro

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AB - We studied the effects of time correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with the backpropagation algorithm. By using a chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with the learning parameter emerges when time-correlated patterns accelerate the learning process.

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JO - Physical Review E

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Effect of time correlation of input patterns on the convergence of on-line learning

Abstract

ASJC Scopus subject areas

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