Adaptive synchronization of neural networks with or without time-varying delay
Adaptive synchronization of neural networks with or without time-varying delay
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作者：
Cao, JD;Lu, JQ
在本文中，基于功能微分方程的不变原理，提出了一种简单，分析和严格的自适应反馈方案，用于与几乎各种耦合的相同神经网络同步，并具有时间变化的延迟，可以是混乱，周期性的，周期性的等等。我们不认为已知连接权重矩阵和延迟连接权重矩阵的混凝土值。我们表明，有或没有时间变化的两个耦合的相同的神经网络可以通过动态增强耦合强度来实现同步。可以正确选择耦合强度的更新增益以调整实现同步的速度。同样，在实践中实现噪声的效果和易于实现，这是非常强大的。此外，还提供了数值模拟以显示提出的同步方法的有效性。 （c）2006年美国物理研究所。
In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method. (C) 2006 American Institute of Physics.