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

机译:带脉冲分流抑制性神经网络的周期解和混沌奇异吸引子

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摘要

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)-Sigma(Ckl is an element of Nr(i,j))C(ij)(kl)f(ij)[x(kl)(t)]x(ij)+L-ij(t), t > 0,t not equal t(k); Delta x(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. (c) 2006 American Institute of Physics.
机译:通过使用重合度理论的连续性定理并构造合适的Lyapunov函数,我们研究了带有脉冲dx(ij)/ dt = -a(ij)的分流抑制性神经网络的周期解的存在性,唯一性和全局指数稳定性。 x(ij)-Sigma(Ckl是Nr(i,j))C(ij)(kl)f(ij)[x(kl)(t)] x(ij)+ L-ij(t)的元素,t> 0,t不等于t(k);增量x(ij)(t(k))= x(ij)(t(k)(+))-x(ij)(t(k)(-))= Ik [x(ij)(t(k ))],k = 1,2,...。此外,数值模拟表明我们的系统可以以多种形式的复杂性发生,包括周期性振荡和混沌奇异吸引子。据我们所知,这是第一次获得这些结果。一些研究人员已将冲激引入其模型,但从未发现类似的结果。 (c)2006年美国物理研究所。

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