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Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties

Chanthorn, Pharunyou, Rajchakit, Grienggrai, Thipcha, Jenjira, Emharuethai, Chanikan, Sriraman, Ramalingam, Lim, Chee Peng and Ramachandran, Raja 2020, Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties, Mathematics, vol. 8, no. 5, pp. 1-19, doi: 10.3390/MATH8050742.

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Title Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties
Author(s) Chanthorn, Pharunyou
Rajchakit, Grienggrai
Thipcha, Jenjira
Emharuethai, Chanikan
Sriraman, Ramalingam
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Ramachandran, Raja
Journal name Mathematics
Volume number 8
Issue number 5
Article ID 742
Start page 1
End page 19
Total pages 19
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2020-05
ISSN 2227-7390
Keyword(s) robust stability
parameter uncertainties
complex-valued neural networks
stochastic disturbances
time-varying delays
Summary In 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.
Language eng
DOI 10.3390/MATH8050742
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30138780

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.