期刊
NEURAL NETWORKS
卷 61, 期 -, 页码 59-67出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.10.003
关键词
Complex-valued neural dynamical system; CR Calculus; Nonlinear convex programming; Complex variables; Global stability analysis
资金
- National Natural Science Foundation of China [61179037, 61473330]
- Doctoral Project of the Ministry of Education of China [20133514110010]
- University of Western Sydney, Australia
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. (C) 2014 Elsevier Ltd. All rights reserved.
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