Zhang neural network for online solution of time-varying convex quadratic program subject to time-varying linear-equality constraints

In this Letter, by following Zhang et al.'s method, a recurrent neural network (termed as Zhang neural network, ZNN) is developed and analyzed for solving online the time-varying convex quadratic-programming problem subject to time-varying linear-equality constraints. Different from conventiona...

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Veröffentlicht in:Physics letters. A Jg. 373; H. 18; S. 1639 - 1643
Hauptverfasser: Zhang, Yunong, Li, Zhan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2009
Schlagworte:
Global convergence
Gradient-based neural network (GNN)
Quadratic programming
Recurrent neural networks
Time-varying
05.10.-a
Recurrent neural networks
Global convergence
41A25
52A41
Gradient-based neural network (GNN)
Time-varying
Quadratic programming
92B20
ISSN:0375-9601, 1873-2429
Online-Zugang:Volltext
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Zusammenfassung:In this Letter, by following Zhang et al.'s method, a recurrent neural network (termed as Zhang neural network, ZNN) is developed and analyzed for solving online the time-varying convex quadratic-programming problem subject to time-varying linear-equality constraints. Different from conventional gradient-based neural networks (GNN), such a ZNN model makes full use of the time-derivative information of time-varying coefficient. The resultant ZNN model is theoretically proved to have global exponential convergence to the time-varying theoretical optimal solution of the investigated time-varying convex quadratic program. Computer-simulation results further substantiate the effectiveness, efficiency and novelty of such ZNN model and method.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0375-9601
1873-2429
DOI:10.1016/j.physleta.2009.03.011