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Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays

机译:大奖Hopfield双向关联内存神经网络与混合延迟的全局消散

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摘要

In this paper, the problem of the global dissipativity of high-order Hopfield bidirectional associative memory neural networks with time-varying coefficients and distributed delays is discussed. By using Lyapunov-Krasovskii functional method, inequality techniques and linear matrix inequalities, a novel set of sufficient conditions for global dissipativity and global exponential dissipativity for the addressed system is developed. Further, the estimations of the positive invariant set, globally attractive set and globally exponentially attractive set are found. Finally, two examples with numerical simulations are provided to support the feasibility of the theoretical findings.
机译:在本文中,讨论了具有时变系数和分布延迟的高阶Hopfield双向联合存储器神经网络的全局消散性问题。 通过使用Lyapunov-Krasovskii功能方法,不等式技术和线性矩阵不等式,开发了一种新颖的全球耗散条件和寻址系统的全球指数耗散条件。 此外,找到了积极不变集,全球有吸引力集和全球指数的有吸引力集的估计。 最后,提供了两个具有数值模拟的示例以支持理论发现的可行性。

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