A novel neurodynamic reaction-diffusion model for solving linear variational inequality problems and its application

Chunlin Sha and Hongyong Zhao

Applied Mathematics and Computation, 2019, vol. 346, issue C, 57-75

Abstract: In this paper, we present a new delayed projection neural network with reaction-diffusion terms for solving linear variational inequality problems. The proposed neural network possesses a simple one-layer structure. By employing the differential inequality technique and constructing a new Lyapunov–Krasovskii functional, we derive some novel sufficient conditions ensuring the globally exponential stability. These conditions are dependent on diffusions and the monotonicity assumption is unnecessary. Furthermore, the considered neural network can solve quadratic programming problems. Finally, several applicable examples are provided to illustrate the satisfactory performance of the proposed neural network.

Keywords: Projection neural network; Delays; Diffusions; Globally exponential stability; Image fusion (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300318308890
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:apmaco:v:346:y:2019:i:c:p:57-75

DOI: 10.1016/j.amc.2018.10.023

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().