Version 1
: Received: 3 April 2020 / Approved: 6 April 2020 / Online: 6 April 2020 (11:06:56 CEST)
How to cite:
Chanthorn, P.; Rajchakit, G.; Thipcha, J.; Emharuethai, C.; Sriraman, R. Further Robust Dissipativity Analysis of Uncertain Stochastic Generalized Neural Networks With Markovian Jump Parameters. Preprints2020, 2020040048 (doi: 10.20944/preprints202004.0048.v1).
Chanthorn, P.; Rajchakit, G.; Thipcha, J.; Emharuethai, C.; Sriraman, R. Further Robust Dissipativity Analysis of Uncertain Stochastic Generalized Neural Networks With Markovian Jump Parameters. Preprints 2020, 2020040048 (doi: 10.20944/preprints202004.0048.v1).
Cite as:
Chanthorn, P.; Rajchakit, G.; Thipcha, J.; Emharuethai, C.; Sriraman, R. Further Robust Dissipativity Analysis of Uncertain Stochastic Generalized Neural Networks With Markovian Jump Parameters. Preprints2020, 2020040048 (doi: 10.20944/preprints202004.0048.v1).
Chanthorn, P.; Rajchakit, G.; Thipcha, J.; Emharuethai, C.; Sriraman, R. Further Robust Dissipativity Analysis of Uncertain Stochastic Generalized Neural Networks With Markovian Jump Parameters. Preprints 2020, 2020040048 (doi: 10.20944/preprints202004.0048.v1).
Abstract
This paper analyzes the robust dissipativity of uncertain stochastic generalized neural networks (USGNNs) with Markovian jumping parameters and time-varying delays. In practical applications most of the systems refer to uncertainties, hence, the norm-bounded parameter uncertainties and stochastic disturbance are considered. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and by employing integral inequalities LMI-based sufficient conditions of the considered systems are established. Numerical simulations are given to show the merit of the presented results.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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