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Robust dissipativity analysis of Hopfield-type complex-valued neural networks with time-varying delays and linear fractional uncertainties

Chanthorn, Pharunyou, Rajchakit, Grienggrai, Ramalingam, Sriraman, Lim, Chee Peng and Ramachandran, Raja 2020, Robust dissipativity analysis of Hopfield-type complex-valued neural networks with time-varying delays and linear fractional uncertainties, Mathematics, vol. 8, no. 4, pp. 1-22, doi: 10.3390/math8040595.

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Title Robust dissipativity analysis of Hopfield-type complex-valued neural networks with time-varying delays and linear fractional uncertainties
Author(s) Chanthorn, Pharunyou
Rajchakit, Grienggrai
Ramalingam, Sriraman
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Ramachandran, Raja
Journal name Mathematics
Volume number 8
Issue number 4
Article ID 595
Start page 1
End page 22
Total pages 22
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2020-04
ISSN 2227-7390
Keyword(s) dissipativity analysis
Hopfield neural networks
integral inequality
time-varying delays
Summary We study the robust dissipativity issue with respect to the Hopfield-type of complex-valued neural network (HTCVNN) models incorporated with time-varying delays and linear fractional uncertainties. To avoid the computational issues in the complex domain, we divide the original complex-valued system into two real-valued systems. We devise an appropriate Lyapunov-Krasovskii functional (LKF) equipped with general integral terms to facilitate the analysis. By exploiting the multiple integral inequality method, the sufficient conditions for the dissipativity of HTCVNN models are obtained via the linear matrix inequalities (LMIs). The MATLAB software package is used to solve the LMIs effectively. We devise a number of numerical models and their empirical results positively ascertain the obtained results.
Language eng
DOI 10.3390/math8040595
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:30137334

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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.