Receding Horizon Synchronization of Delayed Neural Networks Using a Novel Inequality on Quadratic Polynomial Functions

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Chengda Lu ◽  
Xian-Ming Zhang ◽  
Min Wu ◽  
Qing-Long Han ◽  
Yong He
Keyword(s):  
Neural Networks ◽  
Quadratic Polynomial ◽  
Polynomial Functions ◽  
Receding Horizon ◽  
Delayed 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.

Adaptive state estimation of Markov switched neural networks driven by Lévy noise

2019 ◽  
Vol 42 (2) ◽  
pp. 330-336
Author(s):  
Dongbing Tong ◽  
Qiaoyu Chen ◽  
Wuneng Zhou ◽  
Yuhua Xu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Lévy Noise ◽  
Linear Matrix ◽  
Delayed Neural Networks ◽  
Switched Neural Networks ◽  
Inequality Technique ◽  
Levy Noise

This paper proposes the [Formula: see text]-matrix method to achieve state estimation in Markov switched neural networks with Lévy noise, and the method is very distinct from the linear matrix inequality technique. Meanwhile, in light of the Lyapunov stability theory, some sufficient conditions of the exponential stability are derived for delayed neural networks, and the adaptive update law is obtained. An example verifies the condition of state estimation and confirms the effectiveness of results.

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