dc.creatorChiu, Kuo Shou
dc.creatorPinto Jiménez, Manuel
dc.creatorJeng, Jyh Cheng
dc.date.accessioned2018-12-20T14:14:23Z
dc.date.available2018-12-20T14:14:23Z
dc.date.created2018-12-20T14:14:23Z
dc.date.issued2014
dc.identifierActa Applicandae Mathematicae, Volumen 133, Issue 1, 2018, Pages 133-152
dc.identifier15729036
dc.identifier01678019
dc.identifier10.1007/s10440-013-9863-y
dc.identifierhttps://repositorio.uchile.cl/handle/2250/155127
dc.description.abstractThis paper is concerned with existence, uniqueness and global exponential stability of a periodic solution for recurrent neural network described by a system of differential equations with piecewise constant argument of generalized type (in short DEPCAG). The model involves both advanced and delayed arguments. Employing Banach fixed point theorem combined with Green's function and DEPCAG integral inequality of Gronwall type, we obtain some novel sufficient conditions ensuring the existence as well as the global convergence of the periodic solution. Our results are new, extend and improve earlier publications. Several numerical examples and simulations are also given to show the feasibility of our results. © 2013 Springer Science+Business Media.
dc.languageen
dc.publisherKluwer Academic Publishers
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceActa Applicandae Mathematicae
dc.subjectAsymptotic stability
dc.subjectGlobal exponential stability
dc.subjectPeriodic solutions
dc.subjectPiecewise constant argument of generalized type
dc.subjectRecurrent neural networks
dc.titleExistence and global convergence of periodic solutions in recurrent neural network models with a general piecewise alternately advanced and retarded argument
dc.typeArtículo de revista


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