Journal ArticleDOI
A recurrent neural network for solving Sylvester equation with time-varying coefficients
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TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
Li-Function Activated Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality
Dongsheng Guo,Xinjie Lin +1 more
TL;DR: For such a Li-function activated ZNN (LFAZNN) model, theoretical results are provided to show its excellent computational performance on solving the TVLMI and Comparative simulation results with two illustrative examples further substantiate the efficacy of the presented L FAZNN model for TVL MI solving.
Proceedings ArticleDOI
Joint-angle-drift remedy of three-link planar robot arm performing different types of end-effector trajectories
TL;DR: The quadratic-programming (QP) based scheme-formulation is employed to handle the joint-angle-drift problem of a redundant three-link planar robot arm with its end-effector moving along different types of trajectories (e.g., a Lissajous-figure path, a triangular path and an elliptical path).
Proceedings ArticleDOI
An Anti-Disturbance Integral Recursive Neural Network for Solving Time-Varying Matrix Inversion
TL;DR: In this article, an anti-disturbance integral recursive neural network (ADIRNN) is proposed to solve the time-varying matrix for inverse problem, which has strong robustness and can resist disturbances effectively.
Journal ArticleDOI
DRDNN: A robust model for time-variant nonlinear optimization under multiple equality and inequality constraints
TL;DR: In this article , a disturbance rejection dynamic neural network (DRDNN) is proposed to handle nonlinear optimization with multiple equality and inequality constraints concerning external disturbances, which is a bottleneck problem due to its high complexity.
Journal ArticleDOI
Solvability conditions for mixed sylvester equations in rings
Huaxi Chen,Long Wang,Qian Wang +2 more
TL;DR: Wang and He as mentioned in this paper considered the same problem in the setting of a regular ring and presented necessary and sufficient conditions for the solvability to mixed Sylvester equations in rings. But they did not consider the general case.
References
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Book
Topics in Matrix Analysis
TL;DR: The field of values as discussed by the authors is a generalization of the field of value of matrices and functions, and it includes singular value inequalities, matrix equations and Kronecker products, and Hadamard products.
Book ChapterDOI
Output regulation of nonlinear systems
TL;DR: In this paper, the problem of controlling a fixed nonlinear plant in order to have its output track (or reject) a family of reference (or disturbance) signal produced by some external generator is discussed.
Journal ArticleDOI
Nonlinear control via approximate input-output linearization: the ball and beam example
TL;DR: In this paper, an approximate input-output linearization of nonlinear systems which fail to have a well defined relative degree is studied, and a method for constructing approximate systems that are input output linearizable is provided.
Journal ArticleDOI
Pole assignment via Sylvester's equation
TL;DR: In this article, it was shown that the pole assignment problem can be reduced to solving the linear matrix equations AX − XA = −BG, FX = G successively for X, and then F for almost any choice of G.
Journal ArticleDOI
Neural networks for solving systems of linear equations and related problems
Andrzej Cichocki,Rolf Unbehauen +1 more
TL;DR: Various circuit architectures of simple neuron-like analog processors are considered for online solving of a system of linear equations with real constant and/or time-variable coefficients and can be used for solving linear and quadratic programming problems.
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