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

Long Jin, Yunong Zhang, Shuai Li, Yinyan Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

123 Citations (Scopus)


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.
Original languageEnglish
Article number7468512
Pages (from-to)992-997
Number of pages6
JournalIEEE Transactions on Automatic Control
Issue number2
Publication statusPublished - 1 Feb 2017


  • Control-Theoretic approach
  • noise-Tolerant zeroing neural network (NTZNN)
  • numerical methods
  • time-varying problem solving
  • zero-finding methods

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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