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Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-03
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Many models of neural networks have been extended to complex-valued neural networks.
A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network.
Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images.
The CHNNs are often trapped into the local minima, and their noise tolerance is low.
Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima.
In the present work, we propose a new recall algorithm that eliminates the local minima.
We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations.
American Psychological Association (APA)
Kobayashi, Masaki. 2017. Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1140978
Modern Language Association (MLA)
Kobayashi, Masaki. Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1140978
American Medical Association (AMA)
Kobayashi, Masaki. Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1140978
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140978