Gradient-based neural networks for solving periodic Sylvester matrix equations

DOI10.1016/j.jfranklin.2022.05.023zbMath1505.65178OpenAlexW4280530311MaRDI QIDQ2109185

Lei Zhang, Fengrui Zhang, Lingling Lv, Jinbo Chen

Publication date: 20 December 2022

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2022.05.023

Mathematics Subject Classification ID

Matrix equations and identities (15A24) Numerical methods for matrix equations (65F45)

Related Items (10)

Block-row and block-column iterative algorithms for solving linear matrix equation ⋮ A novel finite-time complex-valued zeoring neural network for solving time-varying complex-valued Sylvester equation ⋮ Nonlinear function activated GNN versus ZNN for online solution of general linear matrix equations ⋮ Weight splitting iteration methods to solve quadratic nonlinear matrix equation \(MY^2+NY+P=0\) ⋮ An improved gradient neural network for solving periodic Sylvester matrix equations ⋮ On the minimum-norm least squares solution of the complex generalized coupled Sylvester matrix equations ⋮ Cyclic gradient based iterative algorithm for a class of generalized coupled Sylvester-conjugate matrix equations ⋮ A modified noise-tolerant ZNN model for solving time-varying Sylvester equation with its application to robot manipulator ⋮ Explicit solutions of conjugate, periodic, time-varying Sylvester equations ⋮ Factor gradient iterative algorithm for solving a class of discrete periodic Sylvester matrix equations

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