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A recurrent neural network for solving Sylvester equation with time-varying coefficients
journal contributionposted on 2023-05-16, 16:37 authored by Zhang, Y, Jiang, D, Wang, J
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.
Publication titleIEEE Transactions on Neural Networks
Department/SchoolSchool of Engineering
PublisherInstitute of Electrical and Electronics Engineers, Inc
Place of publicationUnited States