KFU. Card publication. NEURAL NETWORKS WITH PSEUDO-RANDOM DISTRIBUTION OF RELATIONSHIPS USING THE EXAMPLE OF MERCURY ELECTROLYZER OPERATION MODE MODELING

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NEURAL NETWORKS WITH PSEUDO-RANDOM DISTRIBUTION OF RELATIONSHIPS USING THE EXAMPLE OF MERCURY ELECTROLYZER OPERATION MODE MODELING

Form of presentation

Articles in international journals and collections

Year of publication

2019

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Authors, employees of KFU

Medvedeva Olga Anatolievna, author

Bibliographic description in the original language

Medvedeva Olga Anatolievna, Ivanov Aleksandr Nikolaevich, Morozkin Nikolay Danilovich, Svetlana Anatol'evna Mustafina. NEURAL NETWORKS WITH PSEUDO-RANDOM DISTRIBUTION OF RELATIONSHIPS USING THE EXAMPLE OF MERCURY ELECTROLYZER OPERATION MODE MODELING // INTERNATIONAL TRANSACTION JOURNAL OF ENGINEERING MANAGEMENT & APPLIED SCIENCES & TECHNOLOGIES. - 2019. - Vol.10, Is.16. - Art. �10A16A.

Annotation

This article analyzed the applicability of artificial neural networks to solve the problems of physicochemical process modeling using the example of the mercury electrolyzer operation mode used in caustic soda production. This paper also described the basic qualities of the existing neural networks and the ways of their training. The authors propose the solution to the problem of modeling based on the networks with pseudo-random distribution of connections. This paper described the architecture of these networks, three learning algorithms are proposed. The implementation of neural networks with pseudo-random distribution of connections was performed by Python programming language. The article presents the comparative learning results of different networks with different sets of hyperparameters. Also, the determination of the optimal settings of neural networks allows achieving high learning efficiency. The resulting neural network model described the electrolysis process adequately in accordance with the available source data.

Keywords

Neural networks Modeling, Machine learning, Hyperparameters, Electrolysis, Pseudorandom distribution of connections.

The name of the journal

INTERNATIONAL TRANSACTION JOURNAL OF ENGINEERING MANAGEMENT & APPLIED SCIENCES & TECHNOLOGIES

URL

http://tuengr.com/V10A/10A16A.pdf

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=216273&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Medvedeva Olga Anatolievna	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Medvedeva Olga Anatolievna, Ivanov Aleksandr Nikolaevich, Morozkin Nikolay Danilovich, Svetlana Anatol'evna Mustafina. NEURAL NETWORKS WITH PSEUDO-RANDOM DISTRIBUTION OF RELATIONSHIPS USING THE EXAMPLE OF MERCURY ELECTROLYZER OPERATION MODE MODELING // INTERNATIONAL TRANSACTION JOURNAL OF ENGINEERING MANAGEMENT & APPLIED SCIENCES & TECHNOLOGIES. - 2019. - Vol.10, Is.16. - Art. �10A16A.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=216273&p_lang=2	ru_RU
dc.description.abstract	INTERNATIONAL TRANSACTION JOURNAL OF ENGINEERING MANAGEMENT & APPLIED SCIENCES & TECHNOLOGIES	ru_RU
dc.description.abstract	This article analyzed the applicability of artificial neural networks to solve the problems of physicochemical process modeling using the example of the mercury electrolyzer operation mode used in caustic soda production. This paper also described the basic qualities of the existing neural networks and the ways of their training. The authors propose the solution to the problem of modeling based on the networks with pseudo-random distribution of connections. This paper described the architecture of these networks, three learning algorithms are proposed. The implementation of neural networks with pseudo-random distribution of connections was performed by Python programming language. The article presents the comparative learning results of different networks with different sets of hyperparameters. Also, the determination of the optimal settings of neural networks allows achieving high learning efficiency. The resulting neural network model described the electrolysis process adequately in accordance with the available source data.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Neural networks Modeling	ru_RU
dc.subject	Machine learning	ru_RU
dc.subject	Hyperparameters	ru_RU
dc.subject	Electrolysis	ru_RU
dc.subject	Pseudorandom distribution of connections.	ru_RU
dc.title	NEURAL NETWORKS WITH PSEUDO-RANDOM DISTRIBUTION OF RELATIONSHIPS USING THE EXAMPLE OF MERCURY ELECTROLYZER OPERATION MODE MODELING	ru_RU
dc.type	Articles in international journals and collections	ru_RU