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

Yuhuan Chen, Chenfu Yi and Jian Zhong

Journal of Applied Mathematics, 2013, vol. 2013, 1-6

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:695647

DOI: 10.1155/2013/695647

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