Varying-parameter Zhang neural network for approximating some expressions involving outer inverses

Predrag S. Stanimirović, Vasilios N. Katsikis, Zhijun Zhang, Shuai Li, Jianlong Chen, Mengmeng Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


A varying-parameter ZNN (VPZNN) neural design is defined for approximating various generalized inverses and expressions involving generalized inverses of complex matrices. The proposed model is termed as CVPZNN(A, F, G) and defined on the basis of the error function which includes three appropriate matrices A,F,G. The CVPZNN(A, F, G) evolution design includes so far defined VPZNN models for computing generalized inverses and also generates a number of matrix expressions involving these generalized inverses. Global and super-exponential convergence properties of the proposed model as well as behaviour of its equilibrium state are investigated. Main contribution of the defined model is its generality. Most important particular cases of the defined model are presented in order to show this fact explicitly. Presented simulation results illustrate generality and effectiveness of the discovered ZNN evolution design.

Original languageEnglish
JournalOptimization Methods and Software
Publication statusPublished - 1 Jan 2019


  • generalized inverses
  • super-exponential convergence
  • time-varying matrix
  • varying-parameter ZNN design
  • Zhang neural network

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Applied Mathematics


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