scholarly journals Almost Periodic Synchronization for Quaternion-Valued Neural Networks with Time-Varying Delays

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yongkun Li ◽  
Xiaofang Meng ◽  
Yuan Ye
Keyword(s):  
Neural Networks ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Time Varying ◽  
Almost Periodic ◽  
Banach’S Fixed Point Theorem ◽  
Inequality Technique ◽  
Lyapunov Functional Method ◽  
Linear Differential

This paper focuses on the global exponential almost periodic synchronization of quaternion-valued neural networks with time-varying delays. By virtue of the exponential dichotomy of linear differential equations, Banach’s fixed point theorem, Lyapunov functional method, and differential inequality technique, some sufficient conditions are established for assuring the existence and global exponential synchronization of almost periodic solutions of the delayed quaternion-valued neural networks, which are completely new. Finally, we give one example with simulation to show the applicability and effectiveness of our main results.

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.

scholarly journals Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays

2011 ◽  
Vol 21 (4) ◽  
pp. 649-658 ◽  
Author(s):  
Qianhong Zhang ◽  
Lihui Yang ◽  
Daixi Liao
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Coincidence Degree ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Inequality Technique

Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.

scholarly journals Global Exponential Stability of Almost Periodic Solutions for SICNNs with Continuously Distributed Leakage Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Hong Zhang ◽  
Mingquan Yang
Keyword(s):  
Exponential Stability ◽  
Periodic Solutions ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays ◽  
Lyapunov Functional Method ◽  
Inequality Techniques

Shunting inhibitory cellular neural networks (SICNNs) are considered with the introduction of continuously distributed delays in the leakage (or forgetting) terms. By using the Lyapunov functional method and differential inequality techniques, some sufficient conditions for the existence and exponential stability of almost periodic solutions are established. Our results complement with some recent ones.

scholarly journals Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Haiyong Zheng ◽  
Bin Wu ◽  
Tengda Wei ◽  
Linshan Wang ◽  
Yangfan Wang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Differential Inequality ◽  
Time Delays ◽  
Functional Method ◽  
High Order ◽  
Distributed Time Delays ◽  
Inequality Technique ◽  
Global Exponential Robust Stability ◽  
Lyapunov Functional Method

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope.

The anti-periodic oscillations of shunting inhibitory cellular neural networks with time-varying delays and continuously distributed delays

2017 ◽  
Vol 10 (4) ◽  
pp. 513-529
Author(s):  
Changjin Xu ◽  
Peiluan Li
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Cellular Neural Networks ◽  
Distributed Delays ◽  
Time Varying ◽  
Content Type ◽  
All Solutions ◽  
Inequality Technique

Purpose The purpose of this paper is to study the existence and exponential stability of anti-periodic solutions of a class of shunting inhibitory cellular neural networks (SICNNs) with time-varying delays and continuously distributed delays. Design/methodology/approach The inequality technique and Lyapunov functional method are applied. Findings Sufficient conditions are obtained to ensure that all solutions of the networks converge exponentially to the anti-periodic solution, which are new and complement previously known results. Originality/value There are few papers that deal with the anti-periodic solutions of delayed SICNNs with the form negative feedback – aij(t)αij(xij(t)).

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.

NEW CRITERIA OF ALMOST PERIODIC SOLUTION FOR BAM NEURAL NETWORKS WITH DELAYS AND IMPULSIVE EFFECTS

2007 ◽  
Vol 17 (05) ◽  
pp. 395-406 ◽  
Author(s):  
JUN CHEN ◽  
BAOTONG CUI ◽  
YAN JI
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Lyapunov Functional ◽  
Global Exponential Stability ◽  
Sufficient Conditions ◽  
Almost Periodic Solution ◽  
Time Varying ◽  
Almost Periodic ◽  
Inequality Technique ◽  
New Criteria

This paper presents some sufficient conditions for the existence and global exponential stability of the almost periodic solution for impulsive bi-directional associative memory neural networks with time-varying delays by using Lyapunov functional and Gronwall-Bellmans inequality technique. Comparing with known literatures, the results of this paper are new and they complement previously known results.

The Globally Asymptotic Stability Analysis of the Delayed Recurrent Neural Networks Stability Criterion via LMI Technique and Nonsmooth Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 2030-2033
Author(s):  
Zhen Cai Li ◽  
Yang Wang
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Recurrent Neural Networks ◽  
Nonsmooth Analysis ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Linear Matrix ◽  
Time Varying ◽  
Globally Asymptotic Stability ◽  
Lyapunov Functional Method

This paper considers the problem of globally asymptotic stability of the recurrent neural networks with time-varying delays. A linear matrix inequality (LMI) technology and Lyapunov functional method is employed by combing the means of the nonsmooth analysis. A few new sufficient conditions and criterions were proposed to ensure the delayed recurrent neural networks are uniqueness and globally asymptotic stability of their equilibrium point. A few simulation examples are presented to demonstrate the effectiveness of the results and to improve feasibility.

scholarly journals Exponential Convergence for Cellular Neural Networks with Time-Varying Delays in the Leakage Terms

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Zhibin Chen ◽  
Junxia Meng
Keyword(s):  
Neural Networks ◽  
Differential Inequality ◽  
Exponential Convergence ◽  
Zero Point ◽  
Functional Method ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
All Solutions ◽  
Lyapunov Functional Method ◽  
Inequality Techniques

We consider a class of cellular neural networks with time-varying delays in the leakage terms. By applying Lyapunov functional method and differential inequality techniques, we establish new results to ensure that all solutions of the networks converge exponentially to zero point.

scholarly journals Anti-periodic solutions for fractional-order bidirectional associative memory neural networks with delays

2019 ◽  
Vol 23 (Suppl. 6) ◽  
pp. 2169-2177
Author(s):  
Munevver Tuz ◽  
Gulden Suroglu
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Fractional Order ◽  
Associative Memory ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Bidirectional Associative Memory ◽  
Inequality Technique ◽  
Lyapunov Functional Method

This paper concerns fractional-order bidirectional associative memory neural networks with distributed delays. Based on inequality technique and Lyapunov functional method, some novel sufficient conditions are obtained for the existence and exponential stability of anti-periodic solutions are established. An example is given to show the feasibility main results.

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