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Neural Processing Letters
Published in: Neural Processing Letters 1/2018

09-09-2017

Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses and the Drazin Inverse

Authors: Predrag S. Stanimirović, Marko D. Petković, Dimitrios Gerontitis

Published in: Neural Processing Letters | Issue 1/2018

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Abstract

Two gradient-based recurrent neural networks (GNNs) for solving two matrix equations are presented and investigated. These GNNs can be used for generating various inner inverses, including the Moore–Penrose, and in the computation of the Drazin inverse. Convergence properties of defined GNNs are considered. Certain conditions which impose convergence towards the pseudoinverse, and the Drazin inverse are exactly specified. The influence of nonlinear activation functions on the convergence performance of defined GNN models is investigated. Computer simulation experience further confirms the theoretical results.
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Metadata
Title
Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses and the Drazin Inverse
Authors
Predrag S. Stanimirović
Marko D. Petković
Dimitrios Gerontitis
Publication date
09-09-2017
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2018
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9705-4

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