Please use this identifier to cite or link to this item:
Title: Modeling hyperchaotic datasets for neural networks
Authors: Shiriaev, E. M.
Ширяев, Е. М.
Bezuglova, E. S.
Безуглова, Е. С.
Kucherov, N. N.
Кучеров, Н. Н.
Valuev, G. V.
Валуев, Г. В.
Keywords: Chaos theory;Saito generator;Ressler attractor;Neurocryptography;Hyperchaos;Liapunov generator;Lorentz attractor
Issue Date: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
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
Series/Report no.: Lecture Notes in Networks and Systems
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.
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

Files in This Item:
File SizeFormat 
scopusresults 2207 .pdf
  Restricted Access
63.42 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.