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Neural Processing Letters

Erschienen in:

09.09.2017

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

verfasst von: Predrag S. Stanimirović, Marko D. Petković, Dimitrios Gerontitis

Erschienen in: Neural Processing Letters | Ausgabe 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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Titel: Gradient Neural Network with Nonlinear Activation for Computing Inner Inverses and the Drazin Inverse
verfasst von: Predrag S. Stanimirović
Marko D. Petković
Dimitrios Gerontitis
Publikationsdatum: 09.09.2017
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-017-9705-4

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