Please use this identifier to cite or link to this item:
https://dspace.ncfu.ru/handle/20.500.12258/19639
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shiriaev, E. M. | - |
dc.contributor.author | Ширяев, Е. М. | - |
dc.contributor.author | Bezuglova, E. S. | - |
dc.contributor.author | Безуглова, Е. С. | - |
dc.contributor.author | Kucherov, N. N. | - |
dc.contributor.author | Кучеров, Н. Н. | - |
dc.contributor.author | Valuev, G. V. | - |
dc.contributor.author | Валуев, Г. В. | - |
dc.date.accessioned | 2022-05-31T09:52:14Z | - |
dc.date.available | 2022-05-31T09:52:14Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Shiriaev, E., Bezuglova, E., Kucherov, N., Valuev, G. Modeling hyperchaotic datasets for neural networks // Lecture Notes in Networks and Systems. - 2022. - Том 424. - Стр.: 441 - 453. - DOI10.1007/978-3-030-97020-8_40 | ru |
dc.identifier.uri | http://hdl.handle.net/20.500.12258/19639 | - |
dc.description.abstract | This work is aimed at the studies related to neurocryptography. The paper represents the studies of hyperchaotic mappings and their construction based on the attractors and the research of image noise characteristics using the attractors and their performance. The conducted experiments have demonstrated that Liapunov hyperchaos generator possesses the best performance ratio and noise characteristics. In prospect we are going to conduct the experiments with a compiled data set and neural networks focused on the work with chaotic models and cryptographic algorithms. | ru |
dc.language.iso | en | ru |
dc.publisher | Springer Science and Business Media Deutschland GmbH | ru |
dc.relation.ispartofseries | Lecture Notes in Networks and Systems | - |
dc.subject | Chaos theory | ru |
dc.subject | Saito generator | ru |
dc.subject | Ressler attractor | ru |
dc.subject | Neurocryptography | ru |
dc.subject | Hyperchaos | ru |
dc.subject | Liapunov generator | ru |
dc.subject | Lorentz attractor | ru |
dc.title | Modeling hyperchaotic datasets for neural networks | ru |
dc.type | Статья | ru |
vkr.inst | Факультет математики и компьютерных наук имени профессора Н.И. Червякова | ru |
vkr.inst | Северо-Кавказский центр математических исследований | ru |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
Files in This Item:
File | Size | Format | |
---|---|---|---|
scopusresults 2207 .pdf Restricted Access | 63.42 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.