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An extended analysis on robust dissipativity of uncertain stochastic generalized neural networks with markovian jumping parameters

Humphries, Usa, Rajchakit, Grienggrai, Sriraman, Ramalingam, Kaewmesri, Pramet, Chanthorn, Pharunyou, Lim, Chee Peng and Samidurai, Rajendran 2020, An extended analysis on robust dissipativity of uncertain stochastic generalized neural networks with markovian jumping parameters, Symmetry, vol. 12, no. 6, pp. 1-21, doi: 10.3390/sym12061035.

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Title An extended analysis on robust dissipativity of uncertain stochastic generalized neural networks with markovian jumping parameters
Author(s) Humphries, Usa
Rajchakit, Grienggrai
Sriraman, Ramalingam
Kaewmesri, Pramet
Chanthorn, Pharunyou
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Samidurai, Rajendran
Journal name Symmetry
Volume number 12
Issue number 6
Article ID 1035
Start page 1
End page 21
Total pages 21
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 2073-8994
Keyword(s) neural networks
stochastic disturbance
robust dissipativity
Markovian jump parameters
Summary The main focus of this research is on a comprehensive analysis of robust dissipativity issues pertaining to a class of uncertain stochastic generalized neural network (USGNN) models in the presence of time-varying delays and Markovian jumping parameters (MJPs). In real-world environments, most practical systems are subject to uncertainties. As a result, we take the norm-bounded parameter uncertainties, as well as stochastic disturbances into consideration in our study. To address the task, we formulate the appropriate Lyapunov–Krasovskii functional (LKF), and through the use of effective integral inequalities, simplified linear matrix inequality (LMI) based sufficient conditions are derived. We validate the feasible solutions through numerical examples using MATLAB software. The simulation results are analyzed and discussed, which positively indicate the feasibility and effectiveness of the obtained theoretical findings.
Language eng
DOI 10.3390/sym12061035
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:30145901

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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.