On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay
On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay
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DOI：
10.1109/tnnls.2015.2399421
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发表时间：
2015-02
影响因子：
10.4
通讯作者：
Zhiguang Feng;W. Zheng
中科院分区：
文献类型：
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作者：
Zhiguang Feng;W. Zheng
在此简介中，提出了对离散时间变化的离散时间神经网络的扩展耗散性分析的问题。 ，通过在Lyapunov函数中引入三重词，使用的是L2-L∞的性能和耗散性。三重词，然后建立了带有时间变化的延迟的离散时间神经元网络的扩展耗散标准。降低了所获得的结果的保守主义和有效性。
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 H∞ performance, passivity, l2-l∞ 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.