scholarly journals Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

2020 ◽  
Author(s):  
Chaouki Aouiti ◽  
Qing Hui ◽  
Hediene Jallouli ◽  
Emmanuel Moulay
Keyword(s):  
Neural Networks ◽  
Neutral Type ◽  
Fixed Time ◽  
Time Varying ◽  
Time Varying Delay ◽  
Inertial Neural Networks ◽  
Varying Delay

scholarly journals Exponential Stabilization of Neutral-Type Neural Networks with Interval Nondifferentiable and Distributed Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
W. Weera ◽  
P. Niamsup
Keyword(s):  
Neural Networks ◽  
Neutral Type ◽  
Exponential Stabilization ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Activation Functions ◽  
Delay Function ◽  
Time Varying Delay ◽  
Varying Delay

The problem of exponential stabilization of neutral-type neural networks with various activation functions and interval nondifferentiable and distributed time-varying delays is considered. The interval time-varying delay function is not required to be differentiable. By employing new and improved Lyapunov-Krasovskii functional combined with Leibniz-Newton’s formula, the stabilizability criteria are formulated in terms of a linear matrix inequalities. Numerical examples are given to illustrate and show the effectiveness of the obtained results.

Fixed-time synchronization of quaternion-valued neural networks with time-varying delay

2020 ◽  
Vol 476 (2241) ◽  
pp. 20200324
Author(s):  
Umesh Kumar ◽  
Subir Das ◽  
Chuangxia Huang ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Time Synchronization ◽  
Sufficient Conditions ◽  
Fixed Time ◽  
Time Varying ◽  
Activation Functions ◽  
Time Varying Delay ◽  
The Neural Networks ◽  
Varying Delay

In this article, sufficient conditions for fixed-time synchronization of time-delayed quaternion-valued neural networks (QVNNs) are derived. Firstly, QVNNs are decomposed into four real-valued systems. Then using the available lemmas and by constructing the Lyapunov function, the synchronization criterion for the neural networks is proposed. Activation functions satisfy the Lipschitz condition. A suitable controller has been designed to synchronize the master–slave systems. The effectiveness of the proposed result is validated through a comparison of the settling time obtained by applying two different existing lemmas to a particular problem of synchronization of two identical QVNNs with time-varying delay with the help of suitable controllers.

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