title: A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.
abstract: In this paper, based on 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.
authors.forename: Sitian
authors.initials: S
authors.lastname: Qin
authors.forename: Jiqiang
authors.initials: J
authors.lastname: Feng
authors.forename: Jiahui
authors.initials: J
authors.lastname: Song
authors.forename: Xingnan
authors.initials: X
authors.lastname: Wen
authors.forename: Chen
authors.initials: C
authors.lastname: Xu