Global Exponential Stability of Impulsive Cohen-Grossberg-Type BAM Neural Networks with Time-Varying and Distributed Delays

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dc.contributor.authorBenbourenane, Jamal
dc.contributor.authorAkca, Haydar
dc.contributor.authorCovachev, Valéry
dc.date.accessioned2022-01-06T08:15:14Z
dc.date.accessioned2023-08-19T09:16:17Z
dc.date.available2022-01-06T08:15:14Z
dc.date.available2023-08-19T09:16:17Z
dc.date.issued2014-05
dc.descriptionThe Cohen-Grossberg neural network models proposed by Cohen and Grossberg [1] have been widely applied to various problems in scientific and engineering fields [1]. BAM neural networks are useful in many fields such as pattern recognition and automatic controen_US
dc.description.abstract—The purpose of this paper is to investigate the global exponential stability of a class of impulsive bidirectional associative memories (BAM) neural networks that possesses Cohen-Grossberg dynamics. By constructing and using some inequality techniques and a fixed point theorem sufficient conditions are obtained to ensure the existence and global exponential stability of the solutions for impulsive Cohen-Grossberg neural networks with time delays and distributed delays.en_US
dc.identifier.citationAkça, H., Benbourenane, J., & Covachev, V. (2014). Global Exponential Stability of Impulsive Cohen-Grossberg-Type BAM Neural Networks with Time-Varying and Distributed Delays. International Journal of Applied Physics and Mathematics, 4(3), 196.en_US
dc.identifier.doi10.7763/IJAPM.2014.V4.282
dc.identifier.urihttps://edms.wexl.in/handle/1/2157
dc.language.isoenen_US
dc.publisherIJAPMen_US
dc.subjectCohen-Grossberg neural networksen_US
dc.subjectImpulsesen_US
dc.subjectGglobally exponential stabilityen_US
dc.subjectTime delayen_US
dc.titleGlobal Exponential Stability of Impulsive Cohen-Grossberg-Type BAM Neural Networks with Time-Varying and Distributed Delaysen_US
dc.title.alternativeInternational Journal of Applied Physics and Mathematics, Vol. 4, No. 3en_US
dc.typeArticleen_US

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