Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks

Author

Kobayashi, Masaki

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-5, 5 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-11-01

Country of Publication

Egypt

No. of Pages

5

Main Subjects

Biology

Abstract EN

A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can store multilevel information, such as image data.

Storage capacity is an important problem of Hopfield neural networks.

Jankowski et al.

approximated the crosstalk terms of complex-valued Hopfield neural networks (CHNNs) by the 2-dimensional normal distributions and evaluated their storage capacities.

In this work, we evaluate the storage capacities of TMQHNNs based on their idea.

American Psychological Association (APA)

Kobayashi, Masaki. 2018. Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1130581

Modern Language Association (MLA)

Kobayashi, Masaki. Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-5.
https://search.emarefa.net/detail/BIM-1130581

American Medical Association (AMA)

Kobayashi, Masaki. Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1130581

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1130581