Linear Simultaneous Equations’ Neural Solution and Its Application to Convex Quadratic Programming with Equality-Constraint
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) con...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/695647 |
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| Summary: | 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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| ISSN: | 1110-757X 1687-0042 |