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. Attached Files Name Description MIMEType Size Downloads 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 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 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Thu, 26 Nov 2020, 14:05:16 EST Fri, 27 Nov 2020, 04:03:59 EST Tue, 19 Jan 2021, 05:05:23 EST Thu, 28 Jan 2021, 10:15:09 EST Thu, 28 Jan 2021, 10:18:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 56 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 26 Nov 2020, 14:05:16 EST

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