Back to Search
Start Over
A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Mar2018, Vol. 29 Issue 3, p534-544. 11p. - Publication Year :
- 2018
-
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]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 128240935
- Full Text :
- https://doi.org/10.1109/TNNLS.2016.2635676