A recent work has introduced a class of neural networks for solving linear programming problems, where all trajectories converge toward the global optimal solution in finite time. In this paper, it is shown that global convergence in finite time is robust with respect to tolerances in the electronic implementation, and an estimate of the allowed perturbations preserving convergence is obtained.
DI MARCO, M., Forti, M., Grazzini, M. (2006). Robustness of convergence in finite time for linear programming neural networks. INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 34, 307-316 [10.1002/cta.352].
Robustness of convergence in finite time for linear programming neural networks
DI MARCO, MAURO;FORTI, MAURO;GRAZZINI, MASSIMO
2006-01-01
Abstract
A recent work has introduced a class of neural networks for solving linear programming problems, where all trajectories converge toward the global optimal solution in finite time. In this paper, it is shown that global convergence in finite time is robust with respect to tolerances in the electronic implementation, and an estimate of the allowed perturbations preserving convergence is obtained.
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https://hdl.handle.net/11365/3712
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