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Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties
journal contribution
posted on 2020-05-01, 00:00 authored by P Chanthorn, G Rajchakit, J Thipcha, C Emharuethai, R Sriraman, Chee Peng LimChee Peng Lim, R RamachandranIn practical applications, stochastic effects are normally viewed as the major sources that lead to the system’s unwilling behaviours when modelling real neural systems. As such, the research on network models with stochastic effects is significant. In view of this, in this paper, we analyse the issue of robust stability for a class of uncertain complex-valued stochastic neural networks (UCVSNNs) with time-varying delays. Based on the real-imaginary separate-type activation function, the original UCVSNN model is analysed using an equivalent representation consisting of two real-valued neural networks. By constructing the proper Lyapunov–Krasovskii functional and applying Jensen’s inequality, a number of sufficient conditions can be derived by utilizing It o^ ’s formula, the homeomorphism principle, the linear matrix inequality, and other analytic techniques. As a result, new sufficient conditions to ensure robust, globally asymptotic stability in the mean square for the considered UCVSNN models are derived. Numerical simulations are presented to illustrate the merit of the obtained results.
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Journal
MathematicsVolume
8Issue
5Article number
742Pagination
1 - 19Publisher
MDPI AGLocation
Basel, SwitzerlandPublisher DOI
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2227-7390Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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robust stabilityparameter uncertaintiescomplex-valued neural networksstochastic disturbancestime-varying delaysScience & TechnologyPhysical SciencesMathematicsrobust stabilityparameter uncertaintiescomplex-valued neural networksSQUARE EXPONENTIAL STABILITYDISSIPATIVITY ANALYSISDEPENDENT STABILITYPASSIVITY ANALYSISSTATE ESTIMATIONLEAKAGE DELAYCRITERIONDISCRETE
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