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Global stability in Lagrange sense for BAM-type Cohen–Grossberg neural networks with time-varying delays

机译:具有时变时滞的BAM型Cohen-Grossberg神经网络在Lagrange意义上的全局稳定性

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In this paper, we investigate the positive invariant sets and global exponential attractive sets for a class of bidirectional associative memory (BAM)-type Cohen–Grossberg neural networks with multiple time-varying delays. By applying inequality techniques, some easily verifiable delay-independent criteria for the ultimate boundedness and global exponential attractive sets of BAM-type Cohen–Grossberg neural networks are obtained by constructing appropriate Lyapunov functions. Finally, one example with numerical simulations is given to illustrate the results obtained in this paper.
机译:在本文中,我们研究了一类具有多个时变时滞的双向联想记忆(BAM)型Cohen-Grossberg神经网络的正不变集和全局指数吸引集。通过应用不等式技术,通过构造适当的Lyapunov函数,可以获得一些易于验证的时延独立准则,用于BAM型Cohen-Grossberg神经网络的最终有界性和全局指数吸引集。最后,给出了一个带有数值模拟的例子来说明本文获得的结果。

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