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Stability and Hopf Bifurcation of Multi-ring Coupling Neural Network with Delays

机译:多环耦合神经网络与延迟的稳定性和Hopf分岔

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摘要

Most previous studies with respect to bifurcation dynamics of neural networks are limited to low dimensional or simple single ring structure. It should be noted that neural networks are composed of thousands of neurons, and these network structures are so complex that cannot be accurate representation by a single ring structure or several nodes. Therefore, it has greater practicality and the potential to study the model of high-dimensional or multi-ring coupling neural network. However, how to obtain characteristic equations of high dimensional systems is an unavoidable problem for researchers. In current paper, a model with four rings coupling neural network is proposed, and the stability and Hopf bifurcation of this model are studied. In order to overcome the obstacle of solving characteristic equation by higher order determinant, the Coates flow graph method is used to obtain the characteristic equations of higher order neural network model. Finally, some numerical simulation examples are given to demonstrate the validity of proposed theoretical results, meanwhile the relation between the number of rings and bifurcation point is given by simulation.
机译:关于神经网络的分叉动力学的最先前的研究仅限于低维或简单的单环结构。应该注意的是,神经网络由数千个神经元组成,并且这些网络结构是如此复杂的,其不能通过单环结构或几个节点准确表示。因此,它具有更高的实用性和研究高维或多环耦合神经网络模型的可能性。然而,如何获得高维系统的特征方程是研究人员的不可避免的问题。在本文中,提出了一种具有四个环耦合神经网络的模型,研究了该模型的稳定性和跳跃分叉。为了通过高阶决定仪克服求解特征方程的障碍,使用岩石流程图方法来获得高阶神经网络模型的特征方程。最后,给出了一些数值模拟实施例来证明所提出的理论结果的有效性,同时通过模拟给出环数和分叉点之间的关系。

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