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Bifurcations and chaos in a discrete-time-delayed Hopfield neural network with ring structures and different internal decays

机译:具有环结构和不同内部衰减的离散时滞Hopfield神经网络中的分叉和混沌

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In this paper, a discrete-time-delayed Hopfield type neural network model consisting of p neurons with ring architecture and different internal decays is considered. The stability domain of the null solution is found, the values of characteristic parameter for which bifurcations occur at the origin are identified and the existence of Fold/Cusp, Neimark-Sacker and Flip bifurcations are proved. These bifurcations are analyzed by applying the center manifold theorem and the normal form theory. Probability of resonant 1:3 and 1:4 bifurcations also are proved. It is shown that the dynamics in a neighborhood of the origin become more and more complicated as the characteristic parameter grows in magnitude and passes through the bifurcation values. The occurrence of chaos in the sense of Marotto is shown, if the magnitude of the interconnection coefficients are large enough and at least one of the activation functions has two simple real roots. Some numerical simulations are carried out to illustrate the analytical results.
机译:在本文中,考虑了由具有环结构和不同内部衰减的p个神经元组成的离散时滞Hopfield型神经网络模型。找到零解的稳定域,确定在原点处发生分叉的特征参数的值,并证明存在Fold / Cusp,Neimark-Sacker和Flip分叉。通过应用中心流形定理和法线形式理论来分析这些分叉。还证明了共振1:3和1:4分叉的可能性。结果表明,随着特征参数的大小增加并通过分叉值,原点附近的动力学变得越来越复杂。如果互连系数的大小足够大,并且至少一个激活函数具有两个简单的实根,则表示出现了Marotto混沌。进行了一些数值模拟以说明分析结果。

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