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An Improved Finite-Time and Fixed-Time Stable Synchronization of Coupled Discontinuous Neural Networks
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.2 ) Pub Date : 2021-10-11 , DOI: 10.1109/tnnls.2021.3116320
Qizhen Xiao 1 , Hongliang Liu 1 , Yinkun Wang 2
Affiliation  

This article focuses on the finite-time and fixed-time synchronization of a class of coupled discontinuous neural networks, which can be viewed as a combination of the Hindmarsh–Rose model and the Kuramoto model. To this end, under the framework of Filippov solution, a new finite-time and fixed-time stable theorem is established for nonlinear systems whose right-hand sides may be discontinuous. Moreover, the high-precise settling time is given. Furthermore, by designing a discontinuous control law and using the theory of differential inclusions, some new sufficient conditions are derived to guarantee the synchronization of the addressed coupled networks achieved within a finite-time or fixed-time. These interesting results can be seemed as the supplement and expansion of the previous references. Finally, the derived theoretical results are supported by examples with numerical simulations.

中文翻译:


耦合不连续神经网络的改进有限时间和固定时间稳定同步



本文重点研究一类耦合不连续神经网络的有限时间和固定时间同步,可以将其视为 Hindmarsh-Rose 模型和 Kuramoto 模型的组合。为此,在Filippov解的框架下,针对右侧可能不连续的非线性系统,建立了新的有限时间和定时间稳定定理。此外,还给出了高精度的稳定时间。此外,通过设计不连续控制律并利用微分包含理论,推导了一些新的充分条件,以保证所解决的耦合网络在有限时间或固定时间内实现同步。这些有趣的结果可以看作是对先前参考文献的补充和扩展。最后,得出的理论结果得到数值模拟实例的支持。
更新日期:2021-10-11
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