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H_∞ State Estimation of Static Neural Networks with Mixed Delay

机译:H_∞混合延迟静态神经网络的状态估计

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摘要

This paper focuses on studying the state estimation of static neural networks with mixed delay in which leakage time-varying delay and distributed delay are taken into account, simultaneously. By constructing several suitable Lyapunov-Krasovskii functionals and linear matrix inequality technique, the delay-independent and delay-dependent criteria are established in order that the error system is globally asymptotically stable with H_∞ performance, respectively. In addition, with the skills to construct Lyapunov-Krasovskii functionals, we obtain the results in which we constitutionally drop the differentiability requirement of transmission delays. Some numerical examples are given to show the effectiveness and advantages of the obtained results.
机译:本文重点研究了利用混合延迟的静态神经网络的状态估计,其中泄漏时变延迟和分布延迟同时考虑。通过构建几个合适的Lyapunov-krasovskii功能和线性矩阵不等式技术,建立了延迟独立的和延迟依赖性标准,以便分别使用H_∞性能全局渐近稳定。此外,通过构建Lyapunov-Krasovskii功能的技能,我们获得了我们宪法地降低了传输延误的可怜需求的结果。给出了一些数值例子来显示所得结果的有效性和优点。

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