Nonlinear gradient neural network for solving system of linear equations

Lin Xiao, Kenli Li, Zhiguo Tan, Zhijun Zhang, Bolin Liao, Ke Chen, Long Jin, Shuai Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


For purpose of solving system of linear equations (SoLE) more efficiently, a fast convergent gradient neural network (FCGNN) model is designed and discussed in this paper. Different from the design of the conventional gradient neural network (CGNN), the design of the FCGNN model is based on a nonlinear activation function, and thus the better convergence speed can be reached. In addition, the convergence upper bound of the FCGNN model is estimated and provided in details. Simulative results validate the superiority of the FCGNN model, as compared to the CGNN model for finding SoLE.

Original languageEnglish
Pages (from-to)35-40
Number of pages6
JournalInformation Processing Letters
Publication statusPublished - 1 Feb 2019


  • Fast convergence
  • Gradient neural network (GNN)
  • Nonlinear activation function
  • Performance evaluation
  • Systems of linear equations (SoLE)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Computer Science Applications


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