Synchronization of delayed memristive neural networks by establishing novel Lyapunov functional

2019 ◽  
Vol 369 ◽  
pp. 80-91 ◽  
Author(s):  
Cheng-De Zheng ◽  
Fan Xie
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Memristive Neural Networks

scholarly journals Global exponential synchronization of quaternion-valued memristive neural networks with time delays

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Ruoyu Wei ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Differential Inclusion ◽  
Lyapunov Functional ◽  
Time Delays ◽  
Exponential Synchronization ◽  
The Other ◽  
Memristive Neural Networks ◽  
Global Synchronization ◽  
New Class ◽  
Inequality Techniques

This paper extends the memristive neural networks (MNNs) to quaternion field, a new class of neural networks named quaternion-valued memristive neural networks (QVMNNs) is then established, and the problem of drive-response global synchronization of this type of networks is investigated in this paper. Two cases are taken into consideration: one is with the conventional differential inclusion assumption, the other without. Criteria for the global synchronization of these two cases are achieved respectively by appropriately choosing the Lyapunov functional and applying some inequality techniques. Finally, corresponding simulation examples are presented to demonstrate the correctness of the proposed results derived in this paper.

scholarly journals Memristive Neural Networks: Zeolite‐Based Memristive Synapse with Ultralow Sub‐10‐fJ Energy Consumption for Neuromorphic Computation (Small 13/2021)

Small ◽  
2021 ◽  
Vol 17 (13) ◽  
pp. 2170057
Author(s):  
Tao Zeng ◽  
Xiaoqin Zou ◽  
Zhongqiang Wang ◽  
Guangli Yu ◽  
Zhi Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Memristive Neural Networks

Stabilization for a class of delayed switched inertial neural networks via non-reduced order method

2021 ◽  
pp. 014233122098594
Author(s):  
Xuan Chen ◽  
Dongyun Lin
Keyword(s):  
Neural Networks ◽  
Comparative Research ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Global Stabilization ◽  
Order Method ◽  
Reduced Order ◽  
Global Asymptotic Stabilization ◽  
Inertial Neural Networks ◽  
Barbalat Lemma

This paper tackles the issue of global stabilization for a class of delayed switched inertial neural networks (SINN). Distinct from the frequently employed reduced-order technique, this paper studies SINN directly through non-reduced order method. By constructing a novel Lyapunov functional and using Barbalat Lemma, sufficient conditions for the global asymptotic stabilization issue and global exponential stabilization issue of the considered SINN are established. Numerical simulations further confirm the feasibility of the main results. The comparative research shows that global stabilization results of this paper complement and improve some existing work.

Sign in / Sign up

Export Citation Format

Share Document