RTD-based cellular neural networks with multiple steady states

被引：27

作者：

Itoh, M ^{[1
]}

Julián, P

Chua, LO

机构：

[1] Fukuoka Inst Technol, Dept Informat & Commun Engn, Fukuoka 8110295, Japan

[2] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

来源：

INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS | 2001年 / 11卷 / 12期

关键词：

D O I：

10.1142/S0218127401004133

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we study the relationship between the standard cellular neural network (CNN) and the resonant tunneling diode (RTD)-based CNN. We investigate the functional and advanced capabilities of a new generation of CNNs that exploit the multiplicity of steady states. We also include in the analysis higher order CNNs. Furthermore, some methods for designing RTD-based CNNs with multiple steady states are presented.

引用

页码：2913 / 2959

页数：47

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