Suppr超能文献

具有脉冲的分流抑制细胞神经网络的周期解与混沌奇异吸引子

Periodic solution and chaotic strange attractor for shunting inhibitory cellular neural networks with impulses.

作者信息

Gui Zhanji, Ge Weigao

机构信息

Department of Computer Science, Hainan Normal University, Haikou, HaiNan 571158, People's Republic of China.

出版信息

Chaos. 2006 Sep;16(3):033116. doi: 10.1063/1.2225418.

Abstract

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness, and global exponential stability of periodic solution for shunting inhibitory cellular neural networks with impulses, dx(ij)dt=-a(ij)x(ij)- summation operator(C(kl)inN(r)(i,j))C(ij) (kl)f(ij)[x(kl)(t)]x(ij)+L(ij)(t), t>0,t not equal t(k); Deltax(ij)(t(k))=x(ij)(t(k) (+))-x(ij)(t(k) (-))=I(k)[x(ij)(t(k))], k=1,2,...] . Furthermore, the numerical simulation shows that our system can occur in many forms of complexities, including periodic oscillation and chaotic strange attractor. To the best of our knowledge, these results have been obtained for the first time. Some researchers have introduced impulses into their models, but analogous results have never been found.

摘要

利用重合度理论中的连续定理并构造合适的Lyapunov函数,我们研究了具有脉冲的分流抑制细胞神经网络周期解的存在性、唯一性和全局指数稳定性,(\frac{dx_{ij}}{dt}=-a_{ij}x_{ij}-\sum_{(k,l)\in N_{r}(i,j)}C_{ij}(kl)f_{ij}[x_{kl}(t)]x_{ij}+L_{ij}(t),t > 0,t\neq t_{k};\Delta x_{ij}(t_{k})=x_{ij}(t_{k}^{+})-x_{ij}(t_{k}^{-})=I_{k}[x_{ij}(t_{k})],k = 1,2,\cdots)。此外,数值模拟表明我们的系统可以呈现多种复杂形式,包括周期振荡和混沌奇异吸引子。据我们所知,这些结果是首次获得。一些研究者已将脉冲引入他们的模型,但从未发现类似结果。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验