Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks

2021 ◽  
Vol 407 ◽  
pp. 126308
Author(s):  
Junlan Wang ◽  
Xin Wang ◽  
Yantao Wang ◽  
Xian Zhang
Keyword(s):  
Neural Networks ◽  
High Order ◽  
Proportional Delay ◽  
Order Method ◽  
Stability Criteria ◽  
Reduced Order ◽  
Inertial 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.

Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method

2019 ◽  
Vol 12 (02) ◽  
pp. 1950016 ◽  
Author(s):  
Chuangxia Huang ◽  
Hua Zhang
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Order Method ◽  
Reduced Order ◽  
Varying Coefficients ◽  
Proportional Delays ◽  
Lyapunov Function Method ◽  
Inertial Neural Networks ◽  
Time Varying Coefficients ◽  
Generalized Exponential Stability

This paper, mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients. By combining Lyapunov function method with differential inequality approach, non-reduced order method is used to establish some novel assertions on the existence and generalized exponential stability of periodic solutions for the addressed model. In addition, an example and its numerical simulations are given to support the proposed approach.

New Results on Periodicity of Non-autonomous Inertial Neural Networks Involving Non-reduced Order Method

2019 ◽  
Vol 50 (1) ◽  
pp. 595-606 ◽  
Author(s):  
Chuangxia Huang ◽  
Luanshan Yang ◽  
Bingwen Liu
Keyword(s):  
Neural Networks ◽  
Order Method ◽  
Reduced Order ◽  
Inertial Neural Networks

Non-reduced order method for state estimation of quaternion-valued inertial neural networks

2021 ◽  
Author(s):  
Zhengwen Tu ◽  
Liangwei Wang ◽  
Yongxiang Zhao ◽  
Tao Peng ◽  
Xinsong Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Order Method ◽  
Reduced Order ◽  
Inertial Neural Networks
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