Abstract
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.
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Zhang, Y., Li, X., Li, Z. (2009). Modeling and Verification of Zhang Neural Networks for Online Solution of Time-Varying Quadratic Minimization and Programming. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_12
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DOI: https://doi.org/10.1007/978-3-642-04843-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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