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Published in: Neural Processing Letters 5/2021

28-06-2021

A faster and better robustness zeroing neural network for solving dynamic Sylvester equation

Authors: Jianqiang Gong, Jie Jin

Published in: Neural Processing Letters | Issue 5/2021

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Abstract

In this paper, a new zeroing neural network (NZNN) with a new activation function (AF) is presented and investigated for solving dynamic Sylvester equation (DSE). The proposed NZNN not only finds the solutions of the DSE in fixed-time but also has better robustness, and its superior effectiveness and robustness are proved by rigorous mathematical analysis. Numerical simulation results of the proposed NZNN, the original zeroing neural network activated by other recently reported AFs and the existing robust nonlinear ZNN (RNZNN) for solving second-order dynamic Sylvester equation and third-order dynamic Sylvester equation are provided for the purpose of comparison. Comparing with the existing ZNN models, the proposed NZNN has better robustness and faster convergence performance for solving DSE in the same noise environment. Moreover, a successful robot manipulator trajectory tracking example in noise-disturbed environment using the proposed NZNN is also applied for illustrating its further practical applications.
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Metadata
Title
A faster and better robustness zeroing neural network for solving dynamic Sylvester equation
Authors
Jianqiang Gong
Jie Jin
Publication date
28-06-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 5/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10516-8

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