scholarly journals Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meng Hui ◽  
Jiahuang Zhang ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Rui Yao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Complex Valued

Synchronization of time-varying time delayed neutral-type neural networks for finite-time in complex field

2021 ◽  
Vol 8 (3) ◽  
pp. 486-498
Author(s):  
N. Jayanthi ◽  
◽  
R. Santhakumari ◽  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
State Feedback Control ◽  
Projective Synchronization ◽  
Time Varying ◽  
Master System ◽  
Complex Valued ◽  
Theoretical Results

This paper deals with the problem of finite-time projective synchronization for a class of neutral-type complex-valued neural networks (CVNNs) with time-varying delays. A simple state feedback control protocol is developed such that slave CVNNs can be projective synchronized with the master system in finite time. By employing inequalities technique and designing new Lyapunov--Krasovskii functionals, various novel and easily verifiable conditions are obtained to ensure the finite-time projective synchronization. It is found that the settling time can be explicitly calculated for the neutral-type CVNNs. Finally, two numerical simulation results are demonstrated to validate the theoretical results of this paper.

Finite-time projective synchronization of memristor-based neural networks with leakage and time-varying delays

2019 ◽  
Vol 531 ◽  
pp. 121788 ◽  
Author(s):  
Xiaoli Qin ◽  
Cong Wang ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Projective Synchronization ◽  
Time Varying

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.

Finite-time Synchronization of fractional-order complex-valued fuzzy cellular neural networks with time-varying delays

2021 ◽  
pp. 1-11
Author(s):  
Wenbin Jin ◽  
Wenxia Cui ◽  
Zhenjie Wang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Finite Time ◽  
Time Synchronization ◽  
Sufficient Conditions ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Order Complex ◽  
Fuzzy Cellular Neural Networks ◽  
Complex Valued

Finite-time synchronization is concerned for the fractional-order complex-valued fuzzy cellular neural networks (FOCVFCNNs) with leakage delay and time-varying delays. Without using the usual complex-valued system decomposition method, this paper designs the different forms of the controllers by using 2-norm. And we construct the appropriate Lyapunov functional and apply inequality analytical techniques, some new sufficient conditions are obtained to ensure finite-time synchronization of the FOCVFCNNs. The upper bound of setting-time function is obtained. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

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