A recurrent neural network for computing pseudoinverse matrices

G. Wu, J. Wang, J. Hootman

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


A recurrent neural network is presented for computing pseudoinverse matrices. Under the zero initial state condition, the recurrent neural network derived from a reflexive generalized inverse problem which involves two matrix equations can be used to solve the corresponding pseudoinverse problem which involves four matrix equations. The proposed recurrent neural network based on the reflexive generalized inverse problem simplifies network dynamics and makes physical implementation easier. The proposed recurrent neural network is proven and shown to be asymptotically stable and capable of computing pseudoinverse matrices. Three numerical examples are illustrated to show the performance of the proposed recurrent neural network.

Original languageEnglish
Pages (from-to)13-21
Number of pages9
JournalMathematical and Computer Modelling
Issue number1
Publication statusPublished - Jul 1994
Externally publishedYes


  • Neural networks
  • Pseudoinverse matrices


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