scholarly journals Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication

2021 ◽  
Vol 15 ◽  
Author(s):  
Hui Zhao ◽  
Aidi Liu ◽  
Qingjié Wang ◽  
Mingwen Zheng ◽  
Chuan Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Secure Communication ◽  
Time Synchronization ◽  
Fixed Time ◽  
Settling Time ◽  
Memristive Neural Networks ◽  
Time Stability ◽  
Synchronization Problem ◽  
Communication Scheme ◽  
The Given

This paper explores the realization of a predefined-time synchronization problem for coupled memristive neural networks with multi-links (MCMNN) via nonlinear control. Several effective conditions are obtained to achieve the predefined-time synchronization of MCMNN based on the controller and Lyapunov function. Moreover, the settling time can be tunable based on a parameter designed by the controller, which is more flexible than fixed-time synchronization. Then based on the predefined-time stability criterion and the tunable settling time, we propose a secure communication scheme. This scheme can determine security of communication in the aspect of encrypting the plaintext signal with the participation of multi-links topology and coupled form. Meanwhile, the plaintext signals can be recovered well according to the given new predefined-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the obtained theoretical results and the feasibility of the secure communication scheme.

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

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.

scholarly journals Fixed-Time Synchronization of Neural Networks with Discrete Delay

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shuai Liu ◽  
Chuan Chen ◽  
Haipeng Peng
Keyword(s):  
Neural Networks ◽  
Upper Bound ◽  
Time Synchronization ◽  
Fixed Time ◽  
Settling Time ◽  
Stability Theorem ◽  
Discrete Delay ◽  
Time Stability ◽  
Upper Bound Estimate ◽  
Simple Feedback

In this paper, we establish a new fixed-time stability theorem, which provides a novel fixed-time stability criterion and a novel upper bound estimate formula for the settling time. Numerical simulations show that the upper bound estimate for the settling time in this paper is tighter than those given in the existing fixed-time stability theorems. By designing a simple feedback controller, the fixed-time synchronization of neural networks with discrete delay is investigated based on the fixed-time stability theorem established in this paper. A numerical example is included to validate the effectiveness of the obtained theoretical results.

scholarly journals Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Hao Pu ◽  
Fengjun Li
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Power Exponent ◽  
Memristive Neural Networks ◽  
Synchronization Problem ◽  
Neuron Activation ◽  
Discontinuous Activations ◽  
Inclusion Theory ◽  
Theoretical Results

In this paper, the fixed-time synchronization problem for a class of memristive neural networks with discontinuous neuron activation functions and mixed time-varying delays is investigated. With the help of the fixed-time stability theory, under the framework of Filippov solution and differential inclusion theory, several new and useful sufficient criteria for fixed-time synchronization are obtained by designing two types of energy-saving and simple controllers for the considered systems. Compared with the traditional fixed-time synchronization controller, the controllers used in this paper only have one power exponent term, which is a function of the system state error rather than a constant. Moreover, some previous relevant works are especially improved. Finally, two numerical examples are given to show the correctness and the effectiveness of the obtained theoretical results.

Finite-time synchronization for fuzzy inertial cellular neural networks with time-varying delays via integral inequality

2021 ◽  
pp. 1-14
Author(s):  
Zhenjie Wang ◽  
Wenxia Cui ◽  
Wenbin Jin
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Time Synchronization ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability ◽  
Synchronization Problem ◽  
Inequality Techniques

This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness of the proposed results.

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

2020 ◽  
Vol 123 ◽  
pp. 412-419 ◽  
Author(s):  
Chuan Chen ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Yixian Yang ◽  
Ling Mi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Stability Theorem ◽  
Time Stability

scholarly journals The Fast Fixed-Time Bipartite Synchronization of Coupled Delayed Neural Networks with Signed Graphs

2021 ◽  
Author(s):  
Shiju Yang ◽  
Chuandong Li ◽  
Yu Li ◽  
Ting Yang ◽  
Bo Li
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Lyapunov Stability Theory ◽  
Fixed Time ◽  
Initial Condition ◽  
Signed Graphs ◽  
Comparison System ◽  
Delayed Neural Networks ◽  
Synchronization Problem ◽  
System Method

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.

scholarly journals Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Meng Hui ◽  
Chen Wei ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Ni Luo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Fractional Order ◽  
Finite Time ◽  
Settling Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Synchronization Problem ◽  
Fractional Differential

This paper is concerned with the finite-time projective synchronization problem of fractional-order memristive neural networks (FMNNs) with mixed time-varying delays. Firstly, under the frame of fractional-order differential inclusion and the set-valued map, several criteria are derived to ensure finite-time projective synchronization of FMNNs. Meanwhile, three properties are established to deal with different forms of the finite-time fractional differential inequation, which greatly extend some results on estimation of settling time of FMNNs. In addition to the traditional Lyapunov function with 1-norm form in Theorem 1, a more general and flexible Lyapunov function based on p-norm is constructed in Theorem 2 to analyze the finite-time projective synchronization problem, and the estimation of settling time has been verified less conservative than previous results. Finally, numerical examples are provided to demonstrate the effectiveness of the derived theoretical results.

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