Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64509
Title: Noise-tolerant ZNN models for solving time-varying zero-finding problems : a control-theoretic approach
Authors: Jin, L
Zhang, Y
Li, S 
Zhang, Y
Keywords: Control-theoretic approach
Noise-tolerant zeroing neural network (NTZNN)
Numerical methods
Time–varying problem solving
Zero-finding methods
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on automatic control, 2016, v. 62, no. 2, p. 992-997 How to cite?
Journal: IEEE transactions on automatic control 
Abstract: This technical note proposes a noise-tolerant zeroing neural network (NTZNN) design formula, and shows how recurrent (and recursive) methods for solving time-varying problems can be designed from the viewpoint of control. The NTZNN design formula provides a control-theoretic framework to deal with the convergence, stability and robustness issues of continuous-time (and discrete-time) models. NTZNN models derived from the proposed design formula demonstrate their advantages when applied to solving time-varying zero-finding problems in the presence of noises.
URI: http://hdl.handle.net/10397/64509
ISSN: 0018-9286
EISSN: 1558-2523
DOI: 10.1109/TAC.2016.2566880
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

13
Last Week
1
Last month
Citations as of Nov 16, 2017

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
Citations as of Nov 17, 2017

Page view(s)

38
Last Week
4
Last month
Checked on Nov 12, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.