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 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
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