Gradient-based neural networks for solving periodic Sylvester matrix equations
type: Item
instance of: scholarly article
title: Gradient-based neural networks for solving periodic Sylvester matrix equations
zbMATH Open document ID: 1505.65178
author: Lingling Lv
author: Jinbo Chen
author: Lei Zhang
author: Fengrui Zhang
published in: Journal of the Franklin Institute
publication date: 2022-12-20T00:00:00Z
Mathematics Subject Classification ID: 65F45
Mathematics Subject Classification ID: 15A24
zbMATH DE Number: 7635132
MaRDI profile type: Publication
full work available at URL: j.jfranklin.2022.05.023
OpenAlex ID: W4280530311
cites work: Stability and stabilization of discrete-time periodic linear systems with actuator saturation
cites work: Parametric Pole Assignment for Discrete-time Linear Periodic Systems via Output Feedback
cites work: Control of discrete‐time periodic linear systems with input saturation via multi‐step periodic invariant sets
cites work: A parametric periodic Lyapunov equation with application in semi-global stabilization of discrete-time periodic systems subject to actuator saturation
cites work: Finite-time stabilization for positive Markovian jumping neural networks
cites work: Parametric solutions to generalized periodic Sylvester bimatrix equations
cites work: On the periodic Sylvester equations and their applications in periodic Luenberger observers design
cites work: A parametric poles assignment algorithm for second-order linear periodic systems
cites work: An iterative algorithm for discrete periodic Lyapunov matrix equations
cites work: New Iterative Algorithms for Solving Coupled Markovian Jump Lyapunov Equations
cites work: Solutions to linear bimatrix equations with applications to pole assignment of complex-valued linear systems
cites work: Least‐squares partially bisymmetric solutions of coupled Sylvester matrix equations accompanied by a prescribed submatrix constraint
cites work: Solution to the second-order Sylvester matrix equation MVF/sup 2/+DVF+KV=BW
cites work: Extending the CGLS method for finding the least squares solutions of general discrete-time periodic matrix equations
cites work: Gradient based iterative algorithms for solving a class of matrix equations
cites work: Gradient based iterative algorithm for solving coupled matrix equations
cites work: An SOR implicit iterative algorithm for coupled Lyapunov equations
cites work: Finite iterative solutions to periodic Sylvester matrix equations
cites work: Gradient based approach for generalized discrete-time periodic coupled Sylvester matrix equations
cites work: Performance Analysis of Gradient Neural Network Exploited for Online Time-Varying Matrix Inversion
cites work: The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle
cites work: Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle
cites work: Least squares estimation for a class of non-uniformly sampled systems based on the hierarchical identification principle
cites work: Improved neural solution for the Lyapunov matrix equation based on gradient search
cites work: Nonlinear gradient neural network for solving system of linear equations
cites work: An iterative algorithm for periodic Sylvester matrix equations
cites work: The conjugate gradient methods for solving the generalized periodic Sylvester matrix equations
cites work: Matrix form of Biconjugate Residual Algorithm to Solve the Discrete‐Time Periodic Sylvester Matrix Equations
cites work: A numerical solution of a class of periodic coupled matrix equations
DOI: 10.1016/J.JFRANKLIN.2022.05.023
description: scientific article; zbMATH DE number 7635132