A Discrete-Time Projection Neural Network for Solving Degenerate Convex Quadratic Optimization Academic Article

abstract

• 2016, Springer Science+Business Media New York. This paper presents a discrete-time neural network to solve convex degenerate quadratic optimization problems. Under certain conditions, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution. Compared with the existing continuous-time neural networks for degenerate quadratic optimization, the proposed neural network in this paper is more suitable for hardware implementation. Results of two experiments of this neural network are given to illustrate the effectiveness of the proposed neural network.

authors

• Huang, Tingwen

published proceedings

• CIRCUITS SYSTEMS AND SIGNAL PROCESSING

author list (cited authors)

• Zhang, Z., Li, C., He, X., & Huang, T.

citation count

• 3

complete list of authors

• Zhang, Zican||Li, Chuandong||He, Xing||Huang, Tingwen

publication date

• January 2017

publisher

• Springer Nature  Publisher

published in

• Circuits, Systems and Signal Processing  Journal

keywords

• Algorithm Convergence
• Convex Optimization
• Degenerate Quadratic Optimization
• Discrete-time Neural Network

Digital Object Identifier (DOI)

• 10.1007/s00034-016-0308-5

start page

• 389

end page

• 403

volume

• 36

issue

• 1

URL

• http://dx.doi.org/10.1007/s00034-016-0308-5