Discrete-time analogues of integrodifferential equations modeling neural networks

Changyin Sun; Chun Bo Feng

doi:10.1016/j.physleta.2004.10.082

Discrete-time analogues of integrodifferential equations modeling neural networks

Changyin Sun
, Chun Bo Feng

Hohai University
Southeast University, Nanjing
Chinese University of Hong Kong

科研成果: 期刊稿件 › 文章 › 同行评审

24 引用（Scopus）

摘要

Discrete-time analogues of integrodifferential equations modeling neural networks with periodic inputs are introduced. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the periodicity of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions ensuring that every solutions of the discrete-time analogue converge exponentially to the unique periodic solutions.

源语言	英语
页（从-至）	180-191
页数	12
期刊	Physics Letters, Section A: General, Atomic and Solid State Physics
卷	334
期	2-3
DOI	https://doi.org/10.1016/j.physleta.2004.10.082
出版状态	已出版 - 10 1月 2005
已对外发布	是

访问文件

10.1016/j.physleta.2004.10.082

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{1d4debe31584436eb59bbdd73198d366,

title = "Discrete-time analogues of integrodifferential equations modeling neural networks",

abstract = "Discrete-time analogues of integrodifferential equations modeling neural networks with periodic inputs are introduced. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the periodicity of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions ensuring that every solutions of the discrete-time analogue converge exponentially to the unique periodic solutions.",

keywords = "Delay, Discrete-time analogues, Exponential periodicity",

author = "Changyin Sun and Feng, \{Chun Bo\}",

year = "2005",

month = jan,

day = "10",

doi = "10.1016/j.physleta.2004.10.082",

language = "英语",

volume = "334",

pages = "180--191",

journal = "Physics Letters, Section A: General, Atomic and Solid State Physics",

issn = "0375-9601",

publisher = "Elsevier B.V.",

number = "2-3",

}

TY - JOUR

T1 - Discrete-time analogues of integrodifferential equations modeling neural networks

AU - Sun, Changyin

AU - Feng, Chun Bo

PY - 2005/1/10

Y1 - 2005/1/10

N2 - Discrete-time analogues of integrodifferential equations modeling neural networks with periodic inputs are introduced. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the periodicity of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions ensuring that every solutions of the discrete-time analogue converge exponentially to the unique periodic solutions.

AB - Discrete-time analogues of integrodifferential equations modeling neural networks with periodic inputs are introduced. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the periodicity of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions ensuring that every solutions of the discrete-time analogue converge exponentially to the unique periodic solutions.

KW - Delay

KW - Discrete-time analogues

KW - Exponential periodicity

UR - https://www.scopus.com/pages/publications/11144309685

U2 - 10.1016/j.physleta.2004.10.082

DO - 10.1016/j.physleta.2004.10.082

M3 - 文章

AN - SCOPUS:11144309685

SN - 0375-9601

VL - 334

SP - 180

EP - 191

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

IS - 2-3

ER -

Discrete-time analogues of integrodifferential equations modeling neural networks

摘要

访问文件

其它文件与链接

学术指纹

引用此