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A single-layer asymmetric RNN with low hardware complexity for solving linear equations

Ansari, Mohammad Samar
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University of Chester
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2022-01-25
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Computer Science
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Abstract
A single layer neural network for the solution of linear equations is presented. The proposed circuit is based on the standard Hopfield model albeit with the added flexibility that the interconnection weight matrix need not be symmetric. This results in an asymmetric Hopfield neural network capable of solving linear equations. PSPICE simulation results are given which verify the theoretical predictions. A simple technique to incorporate re-configurability into the circuit for setting the different weights of the interconnection is also included. Experimental results for circuits set up to solve small problems further confirm the operation of the proposed circuit.
Citation
Ansari, M. S. (2022). A single-layer asymmetric RNN with low hardware complexity for solving linear equations. Neurocomputing, 485, 74-88. https://doi.org/10.1016/j.neucom.2022.01.033
Publisher
Elsevier
Journal
Neurocomputing
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URI
http://hdl.handle.net/10034/627191
DOI
10.1016/j.neucom.2022.01.033
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Article
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ISSN
0925-2312
EISSN
1872-8286
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https://www.sciencedirect.com/science/article/abs/pii/S0925231222000509