Presentation 1996/2/2
Time Series Analysis using Radial Basis Function Networks
Satoshi Ogawa, Tohru Ikeguchi, Takeshi Matozaki, Kazuyuki Aihara,
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Abstract(in English) In this paper deterministic nonlinear prediction is applied to artificial time series data and real data of squid axon response using radial basis function networks. We adopted smoothing radial basis interpolation which considers both faithfulness and smoothness of interpolation which is suitable under existence of noise. In order to take an optimal balance between faithfulness and smoothness of interpolation, cross validation is applied. As a result, it is confirmed that the smoothing radial basis interpolation by cross validation is more effective than standard one.
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Keyword(in English) Radial basis function / Time series analysis / Chaos / Cross validation / Bifurcation / Prediction
Paper # NC95-95
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Committee NC
Conference Date 1996/2/2(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Time Series Analysis using Radial Basis Function Networks
Sub Title (in English)
Keyword(1) Radial basis function
Keyword(2) Time series analysis
Keyword(3) Chaos
Keyword(4) Cross validation
Keyword(5) Bifurcation
Keyword(6) Prediction
1st Author's Name Satoshi Ogawa
1st Author's Affiliation Department of Applied Electronics, Science University of Tokyo()
2nd Author's Name Tohru Ikeguchi
2nd Author's Affiliation Department of Applied Electronics, Science University of Tokyo
3rd Author's Name Takeshi Matozaki
3rd Author's Affiliation Department of Applied Electronics, Science University of Tokyo
4th Author's Name Kazuyuki Aihara
4th Author's Affiliation Department of Mathematical Engineering and Information, University of Tokyo
Date 1996/2/2
Paper # NC95-95
Volume (vol) vol.95
Number (no) 505
Page pp.pp.-
#Pages 8
Date of Issue