Creation of input data sets when constructing neural network models

Yurii A. Polozov

doi:10.1117/12.3036472

31 July 2024 Creation of input data sets when constructing neural network models

Yurii A. Polozov

Proceedings Volume 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024); 132170U (2024) https://doi.org/10.1117/12.3036472
Event: Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 2024, Fergana and Bukhara, Uzbekistan

Abstract

The paper solves the problem of reducing the dimension of input features when constructing neural models for the SuperMAG electrojet index forecast. In order to decrease the input data dimension, estimates of correlation indexes values and computations of neural network errors are used. Feature sets providing the least error for the SuperMAG electrojet index modeling are obtained. Modelling of the geomagnetic data was performed based on the obtained feature sets. Recurrent neural networks were used for modelling. The dependence of the modelling quality on the input data is shown.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yurii A. Polozov "Creation of input data sets when constructing neural network models", Proc. SPIE 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 132170U (31 July 2024); https://doi.org/10.1117/12.3036472

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