scholarly journals Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2231
Author(s):  
Jian Zhang ◽  
Ancheng Chang ◽  
Gang Yang
Keyword(s):  
Neural Networks ◽  
Stability Criterion ◽  
Lyapunov Functional ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Multiple Delays ◽  
Application Range ◽  
Research Article ◽  
Sufficient Stability ◽  
Inertial Neural Networks

The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one novel sufficient stability criterion for the existence and global exponential stability of T-periodic solutions on the proposed system is obtained. In addition, an instructive numerical example is arranged to support the present approach. The obtained results broaden the application range of neutral-types inertial neural networks.

scholarly journals Dynamics of Almost Periodic BAM Neural Networks with Neutral Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Yaqin Li
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Lyapunov Functional ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Almost Periodic Solution ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Almost Periodic

The paper investigates the almost periodic oscillatory properties of neutral-type BAM neural networks with time-varying delays. By employing the contracting mapping principle and constructing suitable Lyapunov functional, several sufficient conditions are established for the existence, uniqueness, and global exponential stability of almost periodic solution of the system. The results of this paper are new and a simple example is given to illustrate the effectiveness of the new results.

scholarly journals Periodic solution for neutral-type inertial neural networks with time-varying delays

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Mei Xu ◽  
Bo Du
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
Functional Method ◽  
Time Varying ◽  
Mawhin’S Continuation Theorem ◽  
Existence And Stability ◽  
Inertial Neural Networks ◽  
Lyapunov Functional Method ◽  
Theoretical Results

Abstract In this paper the problems of the existence and stability of periodic solutions of neutral-type inertial neural networks with time-varying delays are discussed by applying Mawhin’s continuation theorem and Lyapunov functional method. Finally, two numerical examples are given to illustrate our theoretical results.

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 The Existence and Global Exponential Stability of Almost Periodic Solutions for Neutral-Type CNNs on Time Scales

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 321 ◽  
Author(s):  
Bing Li ◽  
Yongkun Li ◽  
Xiaofang Meng
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Periodic Solutions ◽  
Time Scales ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays

In this paper, neutral-type competitive neural networks with mixed time-varying delays and leakage delays on time scales are proposed. Based on the contraction fixed-point theorem, some sufficient conditions that are independent of the backwards graininess function of the time scale are obtained for the existence and global exponential stability of almost periodic solutions of neural networks under consideration. The results obtained are brand new, indicating that the continuous time and discrete-time conditions of the network share the same dynamic behavior. Finally, two examples are given to illustrate the validity of the results obtained.

Neutral-type of delayed inertial neural networks and their stability analysis using the LMI Approach

2017 ◽  
Vol 230 ◽  
pp. 243-250 ◽  
Author(s):  
S. Lakshmanan ◽  
C.P. Lim ◽  
M. Prakash ◽  
S. Nahavandi ◽  
P. Balasubramaniam
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Neutral Type ◽  
Lmi Approach ◽  
Inertial Neural Networks
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