Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 13, Issue 5, Pages 1053-1063Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2002.1031938
Keywords
global exponential convergence; matrix inversion; nonlinear output regulation; recurrent neural network; time-varying Sylvester equation
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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.
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