eCite Digital Repository

A recurrent neural network for solving Sylvester equation with time-varying coefficients


Zhang, Y and Jiang, D and Wang, J, A recurrent neural network for solving Sylvester equation with time-varying coefficients, IEEE Transactions on Neural Networks , 13, (5) pp. 1053-1063. ISSN 1045-9227 (2002) [Refereed Article]

DOI: doi:10.1109/TNN.2002.1031938


This paper presents a recurrent neural network for solving the Sylvester equation with time-varying coefficient matrices. The recurrent neural network with implicit dynamics is deliberately developed in the way that its trajectory is guaranteed to converge exponentially to the time-varying solution of a given Sylvester equation. Theoretical results of convergence and sensitivity analysis are presented to show the desirable properties of the recurrent neural network. Simulation results of time-varying matrix inversion and on-line nonlinear output regulation via pole assignment for the ball and beam system and the inverted pendulum on a cart system are also included to demonstrate the effectiveness and performance of the proposed neural network.

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Jiang, D (Dr Danchi Jiang)
ID Code:34367
Year Published:2002
Web of Science® Times Cited:341
Deposited By:Engineering
Deposited On:2005-08-01
Last Modified:2005-08-01

Repository Staff Only: item control page