Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach

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 t...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 62; no. 2; pp. 992 - 997
Main Authors Jin, Long, Zhang, Yunong, Li, Shuai, Zhang, Yinyan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Computational modeling
Control stability
Control theory
Control-theoretic approach
Convergence
Mathematical model
Neural networks
noise-tolerant zeroing neural network (NTZNN)
numerical methods
Numerical models
Robustness
time-varying problem solving
Used goods
zero-finding methods
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2566880