Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions

Cohen-Grossberg neural networks with discontinuous activation functions is considered. Using the property of M-matrix and a generalized Lyapunov-like approach, the uniqueness is proved for state solutions and corresponding output solutions, and equilibrium point and corresponding output equilibrium...

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Main Authors: Yanyan Wang, Jianping Zhou
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2012/109319
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author Yanyan Wang
Jianping Zhou
author_facet Yanyan Wang
Jianping Zhou
author_sort Yanyan Wang
collection DOAJ
description Cohen-Grossberg neural networks with discontinuous activation functions is considered. Using the property of M-matrix and a generalized Lyapunov-like approach, the uniqueness is proved for state solutions and corresponding output solutions, and equilibrium point and corresponding output equilibrium point of considered neural networks. Meanwhile, global exponential stability of equilibrium point is obtained. Furthermore, by contraction mapping principle, the uniqueness and globally exponential stability of limit cycle are given. Finally, an example is given to illustrate the effectiveness of the obtained results.
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institution Kabale University
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spelling doaj-art-943bdcfdb1764f26908e1d6f2ae49daa2025-08-20T03:37:28ZengWileyAbstract and Applied Analysis1085-33751687-04092012-01-01201210.1155/2012/109319109319Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation FunctionsYanyan Wang0Jianping Zhou1School of Mathematics and Physics, Anhui University of Technology, Ma'anshan 243002, ChinaSchool of Computer Science, Anhui University of Technology, Ma'anshan 243002, ChinaCohen-Grossberg neural networks with discontinuous activation functions is considered. Using the property of M-matrix and a generalized Lyapunov-like approach, the uniqueness is proved for state solutions and corresponding output solutions, and equilibrium point and corresponding output equilibrium point of considered neural networks. Meanwhile, global exponential stability of equilibrium point is obtained. Furthermore, by contraction mapping principle, the uniqueness and globally exponential stability of limit cycle are given. Finally, an example is given to illustrate the effectiveness of the obtained results.http://dx.doi.org/10.1155/2012/109319
spellingShingle Yanyan Wang
Jianping Zhou
Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
Abstract and Applied Analysis
title Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
title_full Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
title_fullStr Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
title_full_unstemmed Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
title_short Global Convergence for Cohen-Grossberg Neural Networks with Discontinuous Activation Functions
title_sort global convergence for cohen grossberg neural networks with discontinuous activation functions
url http://dx.doi.org/10.1155/2012/109319
work_keys_str_mv AT yanyanwang globalconvergenceforcohengrossbergneuralnetworkswithdiscontinuousactivationfunctions
AT jianpingzhou globalconvergenceforcohengrossbergneuralnetworkswithdiscontinuousactivationfunctions