A recurrent neural network for solving Sylvester equation with time-varying coefficients.
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 online 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.
Zhang
Yunong
Y
Dept. of Autom. and Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China.
Jiang
Danchi
D
Wang
Jun
J