2002/09/01 by Yunong Zhang, Danchi Jiang, Jun Wang · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Control Systems and Identification #Model Reduction and Neural Networks #Neural Networks and Applications
paper · doi:10.1109/tnn.2002.1031938
openalex publication_date 2002/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.