Convex Optimization
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
A recurrent neural network for solving Sylvester equation with time-varying coefficients
IEEE Transactions on Neural Networks
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
IEEE Transactions on Neural Networks
Quasi-Lagrangian Neural Network for Convex Quadratic Optimization
IEEE Transactions on Neural Networks
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In this paper, by following Zhang et al 's neural-dynamic method proposed formally since March 2001, two recurrent neural networks are generalized to solve online the time-varying convex quadratic-minimization and quadratic-programming (QP) problems, of which the latter is subject to a time-varying linear-equality constraint as an example. In comparison with conventional gradient-based neural networks or gradient neural networks (GNN), the resultant Zhang neural networks (ZNN) can be unified as a superior approach for solving online the time-varying quadratic problems. For the purpose of time-varying quadratic-problems solving, this paper investigates comparatively both ZNN and GNN solvers, and then their unified modeling techniques. The modeling results substantiate well the efficacy of such ZNN models on solving online the time-varying convex QP problems.