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On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay

机译：时滞离散神经网络的扩展耗散性

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In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the performance, passivity, – performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.
机译：在本文中，研究了具有时变时滞的离散时间神经网络的扩展耗散分析问题。提出了离散时间神经网络的扩展耗散性的定义，该定义统一了几种性能指标，例如性能，无源性，性能和耗散性。通过在Lyapunov函数中引入三加和项，利用倒凸方法来限定三加和项的正向差，从而建立了具有时变时滞的离散时间神经网络的扩展耗散性准则。导出的条件不仅保证了扩展的耗散性，而且还保证了神经网络的稳定性。给出两个数值例子，以证明所获得结果的保守性和有效性降低。

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