Deakin University
Browse

File(s) under permanent embargo

Design, verification and robotic application of a novel recurrent neural network for computing dynamic Sylvester equation

journal contribution
posted on 2018-09-01, 00:00 authored by L Xiao, Z Zhang, Zili ZhangZili Zhang, W Li, S Li
To solve dynamic Sylvester equation in the presence of additive noises, a novel recurrent neural network (NRNN) with finite-time convergence and excellent robustness is proposed and analyzed in this paper. As compared with the design process of Zhang neural network (ZNN), the proposed NRNN is based on an ingenious integral design formula activated by nonlinear functions, which are able to expedite the convergence speed and suppress unknown additive noises during the solving process of dynamic Sylvester equation. In addition, the global stability, finite-time convergence and denoising property of the NRNN model are theoretically proved. The upper bound of the finite convergence time for the NRNN model is also estimated in theory. Simulative results further verify the efficiency of the NRNN model, as well as its superior robust and finite-time performance to the conventional ZNN model for dynamic Sylvester equation in front of additive noises. At last, the proposed design method for establishing the NRNN model is successfully applied to kinematical control of robotic manipulator in front of additive noises.

History

Journal

Neural networks

Volume

105

Pagination

185-196

Location

Amsterdam, The Netherlands

ISSN

0893-6080

eISSN

1879-2782

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal, C Journal article

Copyright notice

2018, Elsevier Ltd.

Publisher

Elsevier