Cheng Chao-Jung, Liao Teh-Lu, Yan Jun-Juh, Hwang Chi-Chuan
IEEE Trans Syst Man Cybern B Cybern. 2006 Feb;36(1):209-15. doi: 10.1109/tsmcb.2005.856144.
This paper aims to present a synchronization scheme for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory, and its exponential synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Both the cellular neural networks and Hopfield neural networks with time-varying delays are given as examples for illustration.
本文旨在提出一类时滞神经网络的同步方案，其中涵盖了具有时变延迟的霍普菲尔德神经网络和细胞神经网络。利用李雅普诺夫稳定性理论推导了反馈控制增益矩阵，以实现神经网络驱动-响应结构的指数同步，并且如果某个哈密顿矩阵在虚轴上没有特征值，则可以验证其指数同步条件。该条件可避免求解代数黎卡提方程。文中给出了具有时变延迟的细胞神经网络和霍普菲尔德神经网络作为示例进行说明。
