New fixed‐time synchronization control of discontinuous inertial neural networks via indefinite Lyapunov‐Krasovskii functional method

2020 ◽  
Vol 31 (2) ◽  
pp. 471-495
Author(s):  
Fanchao Kong ◽  
Quanxin Zhu
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Functional Method ◽  
Synchronization Control ◽  
Inertial Neural Networks

scholarly journals Robust Fixed-Time Synchronization for Coupled Delayed Neural Networks with Discontinuous Activations Subject to a Quadratic Polynomial Growth

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lina Yu ◽  
Yunfei Ma ◽  
Yuntong Yang ◽  
Jingchao Zhang ◽  
Chunwei Wang
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Functional Method ◽  
Quadratic Polynomial ◽  
Linear Matrix ◽  
Delayed Neural Networks ◽  
Analysis Technique ◽  
Discontinuous Activations ◽  
Discontinuous Activation Functions

In this paper, we focus on the robust fixed-time synchronization for discontinuous neural networks (NNs) with delays and hybrid couplings under uncertain disturbances, where the growth of discontinuous activation functions is governed by a quadratic polynomial. New state-feedback controllers, which include integral terms and discontinuous factors, are designed. By Lyapunov–Krasovskii functional method and inequality analysis technique, some sufficient criteria, which ensue that networks can realize the robust fixed-time synchronization, are addressed in terms of linear matrix inequalities (LMIs). Moreover, the upper bound of the settling time, which is independent on the initial values, can be determined to any desired values in advance by the configuration of parameters in the proposed control law. Finally, two examples are provided to illustrate the validity of the theoretical results.

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