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

• Published in: Physics letters. A Vol. 373; no. 18; pp. 1639 - 1643
• Main Authors: Zhang, Yunong, Li, Zhan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2009
• Subjects: 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
• DOI: 10.1016/j.physleta.2009.03.011

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.