Synchronization of switched neural networks with mixed delays via impulsive control

2011 ◽  
Vol 44 (10) ◽  
pp. 817-826 ◽  
Author(s):  
Xinsong Yang ◽  
Chuangxia Huang ◽  
Quanxin Zhu
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Mixed Delays ◽  
Switched Neural Networks

Exponential H ∞ filtering for switched neural networks with mixed delays

2014 ◽  
Vol 8 (11) ◽  
pp. 987-995 ◽  
Author(s):  
Ziyi Su ◽  
Li Yu ◽  
Hongxia Wang ◽  
Dan Zhang
Keyword(s):  
Neural Networks ◽  
Mixed Delays ◽  
Switched Neural Networks

New results for uncertain switched neural networks with mixed delays using hybrid division method

2018 ◽  
Vol 307 ◽  
pp. 38-53 ◽  
Author(s):  
Wenqian Xie ◽  
Hong Zhu ◽  
Shouming Zhong ◽  
Hao Chen ◽  
Yuping Zhang
Keyword(s):  
Neural Networks ◽  
Mixed Delays ◽  
Switched Neural Networks

scholarly journals Impulsive Control Via Variable Impulsive Perturbations on a Generalized Robust Stability for Cohen–Grossberg Neural Networks With Mixed Delays

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222890-222899
Author(s):  
Jinde Cao ◽  
Trayan Stamov ◽  
Sotir Sotirov ◽  
Evdokia Sotirova ◽  
Ivanka Stamova
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Impulsive Control ◽  
Mixed Delays ◽  
Impulsive Perturbations

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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