A novel fixed-time stability strategy and its application to fixed-time synchronization control of semi-Markov jump delayed neural networks

Abstract In this paper, the fixed-time bipartite synchronization problem for coupled delayed neural networks with signed graphs is discussed. Different from traditional neural networks, the interactions between nodes of delayed neural networks can be either collaborative or antagonistic. Furthermore, compared with the initial-condition based finite-time synchronization, the settling time is bounded by a constant within fixedtime regardless of the initial condition. It is worth noting that the fixed-time stable network for bipartite synchronization in this paper achieves more faster convergence than most existing publications. By applying constructing comparison system method, Lyapunov stability theory and inequality techniques, some sufficient criteria for fixed-time bipartite synchronization are obtained. Finally, two numerical examples are granted to display the performance of the obtained results.

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A new fixed-time stability theorem and its application to the synchronization control of memristive neural networks

Neurocomputing ◽

10.1016/j.neucom.2019.03.040 ◽

2019 ◽

Vol 349 ◽

pp. 290-300 ◽

Cited By ~ 10

Author(s):

Chuan Chen ◽

Lixiang Li ◽

Haipeng Peng ◽

Yixian Yang ◽

Ling Mi ◽

...

Keyword(s):

Neural Networks ◽

Fixed Time ◽

Stability Theorem ◽

Synchronization Control ◽

Memristive Neural Networks ◽

Time Stability

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Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties

Applied Mathematics and Computation ◽

10.1016/j.amc.2018.09.013 ◽

2019 ◽

Vol 344-345 ◽

pp. 230-242 ◽

Cited By ~ 8

Author(s):

Dian Zhang ◽

Jun Cheng ◽

Jinde Cao ◽

Dan Zhang

Keyword(s):

Neural Networks ◽

Finite Time ◽

Time Synchronization ◽

Synchronization Control ◽

Parametric Uncertainties ◽

Markov Jump

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Fixed-time synchronization of coupled memristive neural networks with multi-links and application in secure communication

10.22541/au.161566909.97882460/v1 ◽

2021 ◽

Author(s):

Hui Zhao ◽

Aidi Liu ◽

Qingjie Wang ◽

Mingwen Zheng ◽

Chuan Chen ◽

...

Keyword(s):

Neural Networks ◽

Secure Communication ◽

Time Synchronization ◽

Fixed Time ◽

Stability Theorem ◽

Memristive Neural Networks ◽

Time Stability ◽

Communication Scheme ◽

Upper Bound Estimate ◽

Effective Conditions

This paper is devoted to investigating the issues of fixed-time synchronization of coupled memristive neural networks with multi-links (MCMNN). Based on the fixed-time stability criterion and the upper bound estimate formula for the settling time, we propose a secure communication scheme. The network with multi-links performance and coupled form increase the complexity of network topology and the unstable of systems, which improve security of communication in the aspect of encrypt the plaintext signal. We design a proper controller and build the Lyapunov function, several effective conditions are obtained to achieve the fixed-time synchronization of MCMNN. Moreover, the settling times can be estimated for fixed-time synchronization without depending on any initial values. Meanwhile, the plaintext signals can be recovered according to the fixed-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the theoretical results in fixed-time synchronization of MCMNN, and an example of a secure communication scheme is given to show the usability and superiority based on fixed-time stability theorem.

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