Presentation 2006-01-24
Prediction of Time Series with CNN
Masatomo INOUE, Yuta WATANABE, Tatsuro SHIMADA, Masayuki YAMAUCHI, Mamoru TANAKA,
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Abstract(in English) We propose the prediction of a chaos attractor from Discrete Time Cellular Neural Network (DT-CNN) learning machine of the differential equations. The coefficients of the equations can be obtained by equilibrium points of the DT-CNN state equation. In this process, House Holder Transformation is used to transform the feedback A-template into tri-diagonal sparse matrix. The differential equations can be solved by Runge-Kutta Method. As a simulation, we predict the chaos attractor of Chua's circuit. We consider how much the attractor is predicted from the observal time sequence based on our method.
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Keyword(in English) Cellular Neural Network / Time Series / Prediction
Paper # NLP2005-105
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Conference Information
Committee NLP
Conference Date 2006/1/17(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Prediction of Time Series with CNN
Sub Title (in English)
Keyword(1) Cellular Neural Network
Keyword(2) Time Series
Keyword(3) Prediction
1st Author's Name Masatomo INOUE
1st Author's Affiliation Department of Electrical and Electronics Engineering, Sophia University()
2nd Author's Name Yuta WATANABE
2nd Author's Affiliation Department of Electrical and Electronics Engineering, Sophia University
3rd Author's Name Tatsuro SHIMADA
3rd Author's Affiliation Engine Management Design Dept. No.2, Engine Engineering Div.II, Powertrain Development Group, Toyota Motor Corporation
4th Author's Name Masayuki YAMAUCHI
4th Author's Affiliation Department of Electrical and Electronics Engineering, Sophia University
5th Author's Name Mamoru TANAKA
5th Author's Affiliation Department of Electrical and Electronics Engineering, Sophia University
Date 2006-01-24
Paper # NLP2005-105
Volume (vol) vol.105
Number (no) 547
Page pp.pp.-
#Pages 6
Date of Issue