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2013 Linear Simultaneous Equations’ Neural Solution and Its Application to Convex Quadratic Programming with Equality-Constraint
Yuhuan Chen, Chenfu Yi, Jian Zhong
J. Appl. Math. 2013: 1-6 (2013). DOI: 10.1155/2013/695647

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.

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Yuhuan Chen. Chenfu Yi. Jian Zhong. "Linear Simultaneous Equations’ Neural Solution and Its Application to Convex Quadratic Programming with Equality-Constraint." J. Appl. Math. 2013 1 - 6, 2013. https://doi.org/10.1155/2013/695647

Information

Published: 2013
First available in Project Euclid: 14 March 2014

MathSciNet: MR3122119
Digital Object Identifier: 10.1155/2013/695647

Rights: Copyright © 2013 Hindawi

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