Journal of Applied Mathematics

Linear Simultaneous Equations’ Neural Solution and Its Application to Convex Quadratic Programming with Equality-Constraint

Yuhuan Chen, Chenfu Yi, and Jian Zhong

Full-text: Open access

Abstract

A gradient-based neural network (GNN) is improved and presented for the linear algebraic equation solving. Then, such a GNN model is used for the online solution of the convex quadratic programming (QP) with equality-constraints under the usage of Lagrangian function and Karush-Kuhn-Tucker (KKT) condition. According to the electronic architecture of such a GNN, it is known that the performance of the presented GNN could be enhanced by adopting different activation function arrays and/or design parameters. Computer simulation results substantiate that such a GNN could obtain the accurate solution of the QP problem with an effective manner.

Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 695647, 6 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394808174

Digital Object Identifier
doi:10.1155/2013/695647

Mathematical Reviews number (MathSciNet)
MR3122119

Citation

Chen, Yuhuan; Yi, Chenfu; Zhong, Jian. Linear Simultaneous Equations’ Neural Solution and Its Application to Convex Quadratic Programming with Equality-Constraint. J. Appl. Math. 2013 (2013), Article ID 695647, 6 pages. doi:10.1155/2013/695647. https://projecteuclid.org/euclid.jam/1394808174


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