Modeling hyperchaotic datasets for neural networks

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 demonstr...

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मुख्य लेखकों:	Shiriaev, E. M., Ширяев, Е. М., Bezuglova, E. S., Безуглова, Е. С., Kucherov, N. N., Кучеров, Н. Н., Valuev, G. V., Валуев, Г. В.
स्वरूप:	Статья
भाषा:	English
प्रकाशित:	Springer Science and Business Media Deutschland GmbH 2022
विषय:	Chaos theory Saito generator Ressler attractor Neurocryptography Hyperchaos Liapunov generator Lorentz attractor
ऑनलाइन पहुंच:	https://dspace.ncfu.ru/handle/20.500.12258/19639
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spelling	ir-20.500.12258-196392022-05-31T09:53:01Z Modeling hyperchaotic datasets for neural networks Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Kucherov, N. N. Кучеров, Н. Н. Valuev, G. V. Валуев, Г. В. Chaos theory Saito generator Ressler attractor Neurocryptography Hyperchaos Liapunov generator Lorentz attractor 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. 2022-05-31T09:52:14Z 2022-05-31T09:52:14Z 2022 Статья 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 http://hdl.handle.net/20.500.12258/19639 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Chaos theory Saito generator Ressler attractor Neurocryptography Hyperchaos Liapunov generator Lorentz attractor
spellingShingle	Chaos theory Saito generator Ressler attractor Neurocryptography Hyperchaos Liapunov generator Lorentz attractor Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Kucherov, N. N. Кучеров, Н. Н. Valuev, G. V. Валуев, Г. В. Modeling hyperchaotic datasets for neural networks
description	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.
format	Статья
author	Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Kucherov, N. N. Кучеров, Н. Н. Valuev, G. V. Валуев, Г. В.
author_facet	Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Kucherov, N. N. Кучеров, Н. Н. Valuev, G. V. Валуев, Г. В.
author_sort	Shiriaev, E. M.
title	Modeling hyperchaotic datasets for neural networks
title_short	Modeling hyperchaotic datasets for neural networks
title_full	Modeling hyperchaotic datasets for neural networks
title_fullStr	Modeling hyperchaotic datasets for neural networks
title_full_unstemmed	Modeling hyperchaotic datasets for neural networks
title_sort	modeling hyperchaotic datasets for neural networks
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	https://dspace.ncfu.ru/handle/20.500.12258/19639
work_keys_str_mv	AT shiriaevem modelinghyperchaoticdatasetsforneuralnetworks AT širâevem modelinghyperchaoticdatasetsforneuralnetworks AT bezuglovaes modelinghyperchaoticdatasetsforneuralnetworks AT bezuglovaes modelinghyperchaoticdatasetsforneuralnetworks AT kucherovnn modelinghyperchaoticdatasetsforneuralnetworks AT kučerovnn modelinghyperchaoticdatasetsforneuralnetworks AT valuevgv modelinghyperchaoticdatasetsforneuralnetworks AT valuevgv modelinghyperchaoticdatasetsforneuralnetworks
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Modeling hyperchaotic datasets for neural networks

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