scholarly journals Exponential Stability of Cohen-Grossberg Neural Networks with Impulse Time Window

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mei Liu ◽  
Haijun Jiang ◽  
Cheng Hu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Numerical Simulations ◽  
Lyapunov Functional ◽  
Time Window ◽  
Time Varying ◽  
Stability Criteria ◽  
Analysis Method ◽  
Functional Theory ◽  
Inequality Technique

This paper concerns the problem of exponential stability for a class of Cohen-Grossberg neural networks with impulse time window and time-varying delays. In our letter, the impulsive effects we considered can stochastically occur at a definitive time window and the impulsive controllers we considered can be nonlinear and even rely on the states of all the neurons. Hence, the impulses here can be more applicable and more general. By utilizing Lyapunov functional theory, inequality technique, and the analysis method, we obtain some novel and effective exponential stability criteria for the Cohen-Grossberg neural networks. These results generalize a few previous known results and numerical simulations are given to show the effectiveness of the derived results.

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 New Delay-Dependent Exponential Stability Criteria for Neural Networks with Mixed Time-Varying Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Wu Wen ◽  
Kaibo Shi
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Time Varying ◽  
Stability Criteria ◽  
Decision Variables ◽  
Mathematical Techniques ◽  
Delay Dependent ◽  
The Relationship

This study is concerned with the problem of new delay-dependent exponential stability criteria for neural networks (NNs) with mixed time-varying delays via introducing a novel integral inequality approach. Specifically, first, by taking fully the relationship between the terms in the Leibniz-Newton formula into account, several improved delay-dependent exponential stability criteria are obtained in terms of linear matrix inequalities (LMIs). Second, together with some effective mathematical techniques and a convex optimization approach, less conservative conditions are derived by constructing an appropriate Lyapunov-Krasovskii functional (LKF). Third, the proposed methods include the least numbers of decision variables while keeping the validity of the obtained results. Finally, three numerical examples with simulations are presented to illustrate the validity and advantages of the theoretical results.

scholarly journals Adaptive Synchronization for a Class of Cellular Neural Networks with Pantograph Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xuehui Mei ◽  
Jiuju Xing ◽  
Haijun Jiang ◽  
Cheng Hu ◽  
Liwei Zhang
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Cellular Neural Networks ◽  
Feedback Controller ◽  
Adaptive Synchronization ◽  
Functional Theory ◽  
Adaptive Feedback ◽  
Synchronization Problem ◽  
Inequality Technique

This paper deals with the synchronization problem for a class of cellular neural networks with pantograph delays. By using Lyapunov functional theory and inequality technique, some new and useful results are obtained for asymptotical synchronization under adaptive feedback controller.

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.

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