1. A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.
- Author
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Qin, Sitian, Feng, Jiqiang, Song, Jiahui, Wen, Xingnan, and Xu, Chen
- Subjects
- *
ARTIFICIAL neural networks , *MATHEMATICAL models , *COMPLEX variables , *CONVERGENCE (Telecommunication) , *COMPUTER networks , *PROGRAM transformation - Abstract
In this paper, based on \mathbb CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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