Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays

We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of...

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Main Authors: Lin Lu, Chaoling Li
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2016/3587271
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spelling doaj-67b14901dfa14b288be463bf1eb594222020-11-24T21:14:33ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732016-01-01201610.1155/2016/35872713587271Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage DelaysLin Lu0Chaoling Li1Research Center of Modern Enterprise Management of Guilin University of Technology, Guilin University of Technology, Guilin 541004, ChinaResearch Center of Modern Enterprise Management of Guilin University of Technology, Guilin University of Technology, Guilin 541004, ChinaWe investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002).http://dx.doi.org/10.1155/2016/3587271
collection DOAJ
language English
format Article
sources DOAJ
author Lin Lu
Chaoling Li
spellingShingle Lin Lu
Chaoling Li
Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
Computational Intelligence and Neuroscience
author_facet Lin Lu
Chaoling Li
author_sort Lin Lu
title Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
title_short Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
title_full Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
title_fullStr Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
title_full_unstemmed Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays
title_sort almost periodic dynamics for memristor-based shunting inhibitory cellular neural networks with leakage delays
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2016-01-01
description We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002).
url http://dx.doi.org/10.1155/2016/3587271
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AT chaolingli almostperiodicdynamicsformemristorbasedshuntinginhibitorycellularneuralnetworkswithleakagedelays
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