A recurrent neural network for solving Sylvester equation with time-varying coefficients

نویسندگان

  • Yunong Zhang
  • Danchi Jiang
  • Jun Wang
چکیده

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.

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عنوان ژورنال:
  • IEEE transactions on neural networks

دوره 13 5  شماره 

صفحات  -

تاریخ انتشار 2002