Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

Qiuhong Xiang, Bolin Liao, Lin Xiao, Long Lin, Shuai Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.

Original languageEnglish
Pages (from-to)755-766
Number of pages12
JournalSoft Computing
Issue number3
Publication statusPublished - 13 Feb 2019


  • Discrete time
  • Dynamic matrix pseudoinverse
  • Noise tolerant
  • Numerical examples
  • Theoretical analysis

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology


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