Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

2020 ◽  
Vol 383 ◽  
pp. 125379 ◽  
Author(s):  
Yuxiao Wang ◽  
Yuting Cao ◽  
Zhenyuan Guo ◽  
Tingwen Huang ◽  
Shiping Wen
Keyword(s):  
Neural Networks ◽  
Sliding Mode ◽  
Memristive Neural Networks ◽  
Sampling Algorithm ◽  
Mode Synchronization ◽  
Event Based

Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control

2020 ◽  
Vol 126 ◽  
pp. 163-169
Author(s):  
Bo Sun ◽  
Shengbo Wang ◽  
Yuting Cao ◽  
Zhenyuan Guo ◽  
Tingwen Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Mode Control

Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control

2020 ◽  
Vol 357 (11) ◽  
pp. 7122-7138
Author(s):  
Yuting Cao ◽  
Shiqin Wang ◽  
Zhenyuan Guo ◽  
Tingwen Huang ◽  
Shiping Wen
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Event Based

scholarly journals Projective Synchronization of Nonidentical Fractional-Order Memristive Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chong Chen ◽  
Zhixia Ding
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Sliding Mode ◽  
Direct Method ◽  
Projective Synchronization ◽  
Sliding Mode Controller ◽  
Memristive Neural Networks ◽  
Lyapunov Direct Method ◽  
Sliding Motion ◽  
Fractional Order Integral

This paper investigates projective synchronization of nonidentical fractional-order memristive neural networks (NFMNN) via sliding mode controller. Firstly, based on the sliding mode control theory, a new fractional-order integral sliding mode controller is designed to ensure the occurrence of sliding motion. Furthermore, according to fractional-order differential inequalities and fractional-order Lyapunov direct method, the trajectories of the system converge to the sliding mode surface to carry out sliding mode motion, and some sufficient criteria are obtained to achieve global projective synchronization of NFMNN. In addition, the conclusions extend and improve some previous works on the synchronization of fractional-order memristive neural networks (FMNN). Finally, a simulation example is given to verify the effectiveness and correctness of the obtained results.

Event-based passification of delayed memristive neural networks

2021 ◽  
Author(s):  
Yuting Cao ◽  
Shiqin Wang ◽  
Zhenyuan Guo ◽  
Tingwen Huang ◽  
Shiping Wen
Keyword(s):  
Neural Networks ◽  
Memristive Neural Networks ◽  
Event Based
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