Name
Abstract | ||
|---|---|---|
In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neunet.2014.10.003 | Neural Networks |
Keywords | Field | DocType |
Complex-valued neural dynamical system,CR Calculus,Nonlinear convex programming,Complex variables,Global stability analysis | Linear dynamical system,Mathematical optimization,Nonlinear system,Projected dynamical system,Quadratic equation,Dynamical systems theory,Random dynamical system,Artificial intelligence,Artificial neural network,Convex optimization,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
61 | C | 0893-6080 |
Citations | PageRank | References |
18 | 0.70 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songchuan Zhang | 1 | 51 | 3.32 |
| Youshen Xia | 2 | 1795 | 123.60 |
| Wei Xing Zheng | 3 | 4266 | 274.73 |