DocumentCode :
440450
Title :
Robustness improvement in binary cellular non-linear network architectures
Author :
Brea, Victor ; Laiho, Mika ; Paasio, Ari
Author_Institution :
Dept. of Electron. & Comput. Sci., Santiago de Compostela Univ., Spain
Volume :
1
fYear :
2005
fDate :
28 Aug.-2 Sept. 2005
Abstract :
This paper introduces a systematic approach to enlarge the robustness in binary cellular nonlinear networks (CNN). In particular, the work is devoted to positive range CNN models with high gain nonlinearity and 1-bit of programmability. The robustness is increased by appropriately modifying the bias/threshold term. The CNN cell model and the robustness improvement method are presented within the framework of threshold logic gate (TLG) design, where they prove to be valuable approaches.
Keywords :
cellular neural nets; logic design; threshold logic; binary cellular nonlinear network; gain nonlinearity; modified bias term; modified threshold term; positive range CNN models; threshold logic gate; Cellular networks; Cellular neural networks; Circuits; Gray-scale; Image processing; Intelligent networks; MOS devices; Neural networks; Power dissipation; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuit Theory and Design, 2005. Proceedings of the 2005 European Conference on
Print_ISBN :
0-7803-9066-0
Type :
conf
DOI :
10.1109/ECCTD.2005.1522932
Filename :
1522932
Link To Document :
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