Lagrange Stability of BAM Quaternion-Valued Inertial Neural Networks via Auxiliary Function-Based Integral Inequalities

2022 ◽  
Author(s):  
Rui Zhao ◽  
Baoxian Wang ◽  
Jigui Jian
Keyword(s):  
Neural Networks ◽  
Auxiliary Function ◽  
Integral Inequalities ◽  
Lagrange Stability ◽  
Inertial Neural Networks

scholarly journals Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Shanmugam Saravanan ◽  
M. Syed Ali ◽  
Ahmed Alsaedi ◽  
Bashir Ahmad
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Auxiliary Function ◽  
Integral Inequalities ◽  
Time Varying ◽  
Numerical Examples ◽  
Finite Time Boundedness

In this paper, we investigated the problem of the finite-time boundedness and finitetime passivity for neural networks with time-varying delays. A triple, quadrable and five integral terms with the delay information are introduced in the new Lyapunov–Krasovskii functional (LKF). Based on the auxiliary integral inequality, Writinger integral inequality and Jensen’s inequality, several sufficient conditions are derived. Finally, numerical examples are provided to verify the effectiveness of the proposed criterion. There results are compared with the existing results. 

Lagrange stability of delayed switched inertial neural networks

2020 ◽  
Vol 381 ◽  
pp. 52-60 ◽  
Author(s):  
Lingyu Wang ◽  
Tingwen Huang ◽  
Qiang Xiao
Keyword(s):  
Neural Networks ◽  
Lagrange Stability ◽  
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.

scholarly journals Inequalities and pth moment exponential stability of impulsive delayed Hopfield neural networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yutian Zhang ◽  
Guici Chen ◽  
Qi Luo
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Exponential Stability ◽  
Hopfield Neural Networks ◽  
New Method ◽  
Integral Inequalities ◽  
Delay Function ◽  
Pth Moment Exponential Stability ◽  
Moment Exponential Stability

AbstractIn this paper, the pth moment exponential stability for a class of impulsive delayed Hopfield neural networks is investigated. Some concise algebraic criteria are provided by a new method concerned with impulsive integral inequalities. Our discussion neither requires a complicated Lyapunov function nor the differentiability of the delay function. In addition, we also summarize a new result on the exponential stability of a class of impulsive integral inequalities. Finally, one example is given to illustrate the effectiveness of the obtained results.

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