Further results on finite-time synchronization of delayed inertial memristive neural networks via a novel analysis method

2020 ◽  
Vol 127 ◽  
pp. 47-57 ◽  
Author(s):  
Lanfeng Hua ◽  
Shouming Zhong ◽  
Kaibo Shi ◽  
Xiaojun Zhang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Memristive Neural Networks ◽  
Analysis Method

scholarly journals New finite-time synchronization analysis of a delayed memristive neurodynamic model

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Dandan Ren ◽  
Aidi Yao
Keyword(s):  
Neural Networks ◽  
Convergence Theorem ◽  
Finite Time ◽  
Time Synchronization ◽  
Switching System ◽  
Memristive Neural Networks ◽  
Analysis Method ◽  
Finite Time Convergence ◽  
Inclusion Theory ◽  
Theoretical Results

Abstract This paper presents theoretical results on the finite-time synchronization of delayed memristive neural networks (MNNs). Compared with existing ones on finite-time synchronization of discontinuous NNs, we directly regard the MNNs as a switching system, by introducing a novel analysis method, new synchronization criteria are established without employing differential inclusion theory and non-smooth finite time convergence theorem. Finally, we give a numerical example to support the effectiveness of the theoretical results.

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