Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay

S. Arunagirinathan and T.H. Lee

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 217, issue C, 109-120

Abstract: This work presents a generalized delay-dependent reciprocally convex inequality (GDDRCI) to analyze the stability problem of neural networks (NNs) with time-varying delay. The proposed GDDRCI with its order m in this research improves the estimation accuracy of a reciprocal convex term and encompasses some existing reciprocally convex inequalities as a special case. Consequently, a novel Lyapunov–Krasovskii functional (LKF), which includes a delay-product type m-dependent term and utilizes the correlated cross-information about the states and nonlinear activation function, is formulated. Since the constructed GDDRCI and LKF, the conservatism of the resulting stability conditions of NNs is further decreased. Finally, four numerical examples are presented to demonstrate the importance of the theoretical results.

Keywords: Neural networks; Lyapunov method; Reciprocally convex inequality (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475423004482
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:217:y:2024:i:c:p:109-120

DOI: 10.1016/j.matcom.2023.10.013

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().