Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks
Author
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
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