Presentation	2001/3/9 Equivalent Property of Discrete-Time Cellular Neural Network Yuichi TANJI,
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Abstract(in English)	In this report, the goal is to consider synchronous/asynchronous DT-CNN computationally. Consequently, it is shown that DT-CNN is corresponding to nonlinear relaxation method for solving the equilibrium equation of the continuous-time CNN, where the networks are categorized into synchronous/asynchronous, by means of varieties for the nonlinear relaxation method. Further, it is shown that asynchronous DT-CNN is superior to asynchronous one from convergence property point of view and memoryless on the hardware implementation. In computer simulation, effectiveness of asynchronous DT-CNN is confirmed in the convergence property.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Discrete-Time Cellular Neural NetWork / Synchronous / Asynchronous / Nonlinear Relaxation Method
Paper #	NLP2000-151
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Conference Information
Committee	NLP
Conference Date	2001/3/9(1days)
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Paper Information
Registration To	Nonlinear Problems (NLP)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Equivalent Property of Discrete-Time Cellular Neural Network
Sub Title (in English)
Keyword(1)	Discrete-Time Cellular Neural NetWork
Keyword(2)	Synchronous
Keyword(3)	Asynchronous
Keyword(4)	Nonlinear Relaxation Method
1st Author's Name	Yuichi TANJI
1st Author's Affiliation	Dept. of Reliability-based Information System Engineering, Kagawa University()
Date	2001/3/9
Paper #	NLP2000-151
Volume (vol)	vol.100
Number (no)	680
Page	pp.pp.-
#Pages	5
Date of Issue