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Globally Asymptotic Stability of a Class of Neutral-Type Neural Networks With Delays

机译:一类中立型时滞神经网络的全局渐近稳定性

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摘要

Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen–Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria.
机译:文献中介绍了一类具有滞后型时滞的系统的几种稳定性条件。但是,对于具有中立型时延的神经网络,尚未提出任何结果。因此,该对应关系研究了一类具有时滞的中立型神经网络的全局渐近稳定性。此类系统包括Hopfield神经网络,细胞神经网络和Cohen-Grossberg神经网络。基于Lyapunov稳定性方法,推导了两个与时滞无关的充分稳定性条件。这些稳定性条件易于检查,并且可以从连接矩阵和网络参数中得出,而无需任何关于互连对称性的假设。给出两个说明性的例子,以证明所提出的稳定性标准的有效性。

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