Name
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. Copyright (C) 2006 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1002/cta.352 | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS |
Keywords | Field | DocType |
neural networks, convergence, robustness, linear programming | Convergence (routing),Mathematical optimization,Control theory,Computer science,Robustness (computer science),Linear programming,Artificial neural network,Finite time | Journal |
Volume | Issue | ISSN |
34 | 3 | 0098-9886 |
Citations | PageRank | References |
11 | 0.65 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauro Di Marco | 1 | 205 | 18.38 |
| Mauro Forti | 2 | 398 | 36.80 |
| Massimo Grazzini | 3 | 131 | 11.01 |