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Dynamical Properties of Discrete-Time Background Neural Networks with Uniform Firing Rate

机译：具有均一发射率的离散背景神经网络的动力学特性

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摘要

The dynamics of a discrete-time background network with uniform firing rate and background input is investigated. The conditions for stability are firstly derived. An invariant set is then obtained so that the nondivergence of the network can be guaranteed. In the invariant set, it is proved that all trajectories of the network starting from any nonnegative value will converge to a fixed point under some conditions. In addition, bifurcation and chaos are discussed. It is shown that the network can engender bifurcation and chaos with the increase of background input. The computations of Lyapunov exponents confirm the chaotic behaviors.
机译：研究了具有均匀点火速率和背景输入的离散时间背景网络的动力学。首先得出稳定性的条件。然后获得不变集合，从而可以保证网络的非分散性。在不变集合中，证明了在某些条件下，从任何非负值开始的网络的所有轨迹都将收敛到固定点。另外，讨论了分叉和混乱。结果表明，随着背景输入的增加，网络会产生分叉和混乱。李雅普诺夫指数的计算证实了混沌行为。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|892794.1-892794.6|共6页
  • 作者

    Min Wan; Jianping Gou; Desong Wang; Xiaoming Wang;

  • 作者单位

    School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China;

    School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China;

    School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China;

    School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, China;

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