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A machine learning approach to the vector prediction of moments of finite normal mixturesстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 30 июня 2021 г.
- Авторы: Gorshenin A., Kuzmin V.
- Журнал: Advances in Intelligent Systems and Computing
- Том: 1127
- Год издания: 2020
- Первая страница: 307
- Последняя страница: 314
- DOI: 10.1007/978-3-030-39216-1_27
- Аннотация: The paper compares different architectures of feedforward neural networks designed for vector forecasting of finite normal mixture moments in problems of approximation of plasma density fluctuations. A vector prediction is constructed for mixture expectation, variance, skewness and kurtosis. Its effectiveness is compared to consecutive scalar forecasts for each moment. The best accuracy in terms of RMSE and MAE have been obtained for configurations with one hidden layer of 200 neu-rons and Adam/Adamax optimizers. In addition, it is demonstrated that neural network effectiveness is increased by using information about other moments without compromising learning speed. This study is focused on the creation of modern computational tools for statistical data processing in important problems of plasma physics.
- Добавил в систему: Горшенин Андрей Константинович