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Toward Optimal Synthesis of Discrete-Time Hopfield Neural Network | IEEE Journals & Magazine | IEEE Xplore

Toward Optimal Synthesis of Discrete-Time Hopfield Neural Network


Abstract:

In this research brief, the relationship between eigenvectors (with {+1, −1} components) of a synaptic weight matrix W and the stable/anti-stable states of discrete-t...Show More

Abstract:

In this research brief, the relationship between eigenvectors (with {+1, −1} components) of a synaptic weight matrix W and the stable/anti-stable states of discrete-time Hopfield associative memory (HAM) is established. Also, the synthesis of W with desired stable/anti-stable states using spectral representation of W in even/odd dimension is discussed when the threshold vector is a non-zero vector. Freedom in choice of eigenvalues is capitalized to improve the noise immunity of the Hopfield neural network (HNN). Also, the problem of optimal synthesis of Hopfield Associative memory is formulated.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 34, Issue: 11, November 2023)
Page(s): 9549 - 9554
Date of Publication: 25 March 2022

ISSN Information:

PubMed ID: 35333718

Funding Agency:


References

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