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首页> 外文期刊>Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science >Stability analysis of interval time-varying delayed neural networks including neutral time-delay and leakage delay
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Stability analysis of interval time-varying delayed neural networks including neutral time-delay and leakage delay

机译:间隔时差延迟神经网络的稳定性分析,包括中立时间延迟和泄漏延迟

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This paper addresses an improved stability criterion for an interval time-delayed neural networks (NNs) including neutral delay and leakage delay. By proposing a suitable Lyapunov-Krasovskii functionals (LKFs) together with the Auxiliary function-based integral inequality (AFBII) and reciprocally convex approach (RCC) approach. The major purpose of this research is put forward to the consideration of inequality techniques together with a suitable LKFs, and mixed with the Leibniz-Newton formula within the structure of linear matrix inequalities (LMIs). It is amazing that, the leakage delay has a disrupting impact on the stability behaviour of such system and they cannot be neglected. Finally, numerical examples have been demonstrated to showing feasibility and applicability of the developed technique. In addition, the developed stability criteria tested for feasibility of the benchmark problem to explore the real-world application in the sense of discrete time-delay and leakage delay as a process variable in the system model. (C) 2018 Elsevier Ltd. All rights reserved.
机译:本文解决了包括中性延迟和泄漏延迟的间隔时间延迟神经网络(NNS)的改进的稳定性标准。通过提出合适的Lyapunov-Krasovskii功能(LKF)以及基于辅助功能的整体不等式(AFBII)和互换凸面的方法(RCC)方法。本研究的主要目的是提出与合适的LKF一起考虑不等式技术,并与Leibniz-Newton公式混合在线性矩阵不等式(LMI)。令人惊讶的是,泄漏延迟对这种系统的稳定行为产生了破坏的影响,它们不能被忽视。最后,已经证明了数值示例以显示开发技术的可行性和适用性。此外,在基准问题的可行性中测试的发达的稳定性标准,以探索离散时间延迟和泄漏延迟感的真实应用程序作为系统模型中的过程变量。 (c)2018年elestvier有限公司保留所有权利。

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