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.

scholarly journals Positive periodic solution for inertial neural networks with time-varying delays

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Feng Duan ◽  
Bo Du
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Sufficient Conditions ◽  
Positive Periodic Solution ◽  
Functional Method ◽  
Time Varying ◽  
Positive Periodic Solutions ◽  
Mawhin’S Continuation Theorem ◽  
Existence And Stability ◽  
Inertial Neural Networks

AbstractIn this paper the problems of the existence and stability of positive periodic solutions of inertial neural networks with time-varying delays are discussed by the use of Mawhin’s continuation theorem and Lyapunov functional method. Some sufficient conditions are obtained for guaranteeing the existence and stability of positive periodic solutions of the considered system. Finally, a numerical example is given to illustrate the effectiveness of the obtained results.

scholarly journals Periodic solution for inertial neural networks with variable parameters

2021 ◽  
Vol 6 (12) ◽  
pp. 13580-13591
Author(s):  
Lingping Zhang ◽  
◽  
Bo Du
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Asymptotic Stability ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Mawhin’S Continuation Theorem ◽  
Variable Parameters ◽  
Inertial Neural Networks ◽  
Lyapunov Functional Method

<abstract><p>We discuss periodic solution problems and asymptotic stability for inertial neural networks with $ D- $operator and variable parameters. Based on Mawhin's continuation theorem and Lyapunov functional method, some new sufficient conditions on the existence and asymptotic stability of periodic solutions are established. Finally, a numerical example verifies the effectiveness of the obtained results.</p></abstract>

scholarly journals Periodic Solution for Impulsive Cellar Neural Networks with Time-Varying Delays in the Leakage Terms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Bingwen Liu ◽  
Shuhua Gong
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Differential Inequality ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Time Varying ◽  
Activation Functions ◽  
Lyapunov Functional Method ◽  
Inequality Techniques

This paper is concerned with impulsive cellular neural networks with time-varying delays in leakage terms. Without assuming bounded and monotone conditions on activation functions, we establish sufficient conditions on existence and exponential stability of periodic solutions by using Lyapunov functional method and differential inequality techniques. Our results are complement to some recent ones.

Synchronization of time-varying time delayed neutral-type neural networks for finite-time in complex field

2021 ◽  
Vol 8 (3) ◽  
pp. 486-498
Author(s):  
N. Jayanthi ◽  
◽  
R. Santhakumari ◽  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
State Feedback Control ◽  
Projective Synchronization ◽  
Time Varying ◽  
Master System ◽  
Complex Valued ◽  
Theoretical Results

This paper deals with the problem of finite-time projective synchronization for a class of neutral-type complex-valued neural networks (CVNNs) with time-varying delays. A simple state feedback control protocol is developed such that slave CVNNs can be projective synchronized with the master system in finite time. By employing inequalities technique and designing new Lyapunov--Krasovskii functionals, various novel and easily verifiable conditions are obtained to ensure the finite-time projective synchronization. It is found that the settling time can be explicitly calculated for the neutral-type CVNNs. Finally, two numerical simulation results are demonstrated to validate the theoretical results of this paper.

scholarly journals Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Wuneng Zhou ◽  
Xueqing Yang ◽  
Jun Yang ◽  
Anding Dai ◽  
Huashan Liu
Keyword(s):  
Neural Networks ◽  
Neutral Type ◽  
Stochastic Perturbation ◽  
Adaptive Controller ◽  
Functional Method ◽  
Markovian Switching ◽  
Adaptive Synchronization ◽  
Linear Matrix ◽  
Lyapunov Functional Method ◽  
New Criterion

The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper.

scholarly journals Passivity Analysis of Coupled Stochastic Neural Networks with Multiweights

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Min Cao ◽  
Xun-Wu Yin ◽  
Wen-He Song ◽  
Xue-Mei Sun ◽  
Cheng-Dong Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Stochastic Systems ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Stochastic Neural Networks ◽  
Analysis Techniques ◽  
Passivity Analysis ◽  
Input And Output ◽  
Lyapunov Functional Method ◽  
Theoretical Results

In this paper, we devote to the investigation of passivity in two types of coupled stochastic neural networks (CSNNs) with multiweights and incompatible input and output dimensions. First, some new definitions of passivity are proposed for stochastic systems that may have incompatible input and output dimensions. By utilizing stochastic analysis techniques and Lyapunov functional method, several sufficient conditions are respectively developed for ensuring that CSNNs without and with multiple delay couplings can realize passivity. Besides, the synchronization criteria for CSNNs with multiweights are established by employing the results of output-strictly passivity. Finally, two simulation examples are given to illustrate the validity of the theoretical results.

Mean Square Asymptotic Stability of Neutral Stochastic Neutral Networks with Multiple Time-Varying Delays

2013 ◽  
Vol 684 ◽  
pp. 579-582
Author(s):  
Xiang Dong Shi
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Stochastic Neural Networks ◽  
Multiple Time ◽  
Time Varying ◽  
Lyapunov Functional Method ◽  
Inequality Techniques ◽  
Neutral Stochastic Neural Networks

The paper considers the problems of almost surely asymptotic stability for neutral stochastic neural networks with multiple time-varying delays. By applying Lyapunov functional method and differential inequality techniques, new sufficient conditions ensuring the existence and almost surely asymptotic stability of neutral stochastic neural networks with multiple time-varying delays are established. The results are shown to be generalizations of some previously published results and are less conservative than existing results.

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