A delay-dividing approach to robust stability of uncertain stochastic complex-valued Hopfield delayed neural networks

• Main Authors: Pharunyou Chanthorn, Grienggrai Rajchakit, Usa Humphries, Pramet Kaewmesri, Ramalingam Sriraman, Chee Peng Lim
• Format: Journal
• Published: 2020
• Subjects: Chemistry; Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085336130&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/70400
• Institution: Chiang Mai University

© 2020 by the author. In scientific disciplines and other engineering applications, most of the systems refer to uncertainties, because when modeling physical systems the uncertain parameters are unavoidable. In viewof this, it is important to investigate dynamical systemswith uncertain parameters. In the present study, a delay-dividing approach is devised to study the robust stability issue of uncertain neural networks. Specifically, the uncertain stochastic complex-valued Hopfield neural network (USCVHNN) with time delay is investigated. Here, the uncertainties of the systemparameters are norm-bounded. Based on the Lyapunov mathematical approach and homeomorphism principle, the sufficient conditions for the global asymptotic stability of USCVHNN are derived. To perform this derivation, we divide a complex-valued neural network (CVNN) into two parts, namely real and imaginary, using the delay-dividing approach. All the criteria are expressed by exploiting the linear matrix inequalities (LMIs). Based on two examples, we obtain good theoretical results that ascertain the usefulness of the proposed delay-dividing approach for the USCVHNN model.