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A delay-dividing approach to robust stability of uncertain stochastic complex-valued Hopfield delayed neural networks
journal contribution
posted on 2020-04-25, 00:00 authored by P Chanthorn, G Rajchakit, U Humphries, P Kaewmesri, R Sriraman, Chee Peng LimChee Peng LimIn scientific disciplines and other engineering applications, most of the systems refer to uncertainties, because when modeling physical systems the uncertain parameters are unavoidable. In view of this, it is important to investigate dynamical systems with uncertain parameters. In the present study, a delay-dividing approach is devised to study the robust stability issue of uncertain neural networks. Specifically, the uncertain stochastic complex-valued Hopfield neural network (USCVHNN) with time delay is investigated. Here, the uncertainties of the system parameters are norm-bounded. Based on the Lyapunov mathematical approach and homeomorphism principle, the sufficient conditions for the global asymptotic stability of USCVHNN are derived. To perform this derivation, we divide a complex-valued neural network (CVNN) into two parts, namely real and imaginary, using the delay-dividing approach. All the criteria are expressed by exploiting the linear matrix inequalities (LMIs). Based on two examples, we obtain good theoretical results that ascertain the usefulness of the proposed delay-dividing approach for the USCVHNN model.
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Journal
SymmetryVolume
12Issue
5Article number
683Pagination
1 - 19Publisher
MDPILocation
Basel, SwitzerlandPublisher DOI
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eISSN
2073-8994Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, the authorsUsage metrics
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complex-valued Hopfield neural networksrobust stabilityparameter uncertaintiesstochastic effectsScience & TechnologyMultidisciplinary SciencesScience & Technology - Other TopicsGLOBAL ASYMPTOTIC STABILITYSQUARE EXPONENTIAL STABILITYTIME-VARYING DELAYSDISSIPATIVITY ANALYSISDEPENDENT STABILITYPASSIVITY ANALYSISSTATE ESTIMATIONLEAKAGE DELAYCRITERIONDISCRETE
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