Abstract
When a multi-parameter inverse problem is solved with artificial neural networks, it is usually solved separately for each determined parameter (autonomous determination). In their preceding studies, the authors have demonstrated that joining parameters into groups with simultaneous determination of the values of all parameters within each group may in some cases improve the precision of solution of inverse problems. In this study, the observed effect has been investigated in respect to its resistance to noise in data. The study has been performed at the example of the inverse problem of magnetotellurics, which has a high dimensionality.
This study has been performed at the expense of the grant of Russian Science Foundation (project no. 14-11-00579).
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Isaev, I., Obornev, E., Obornev, I., Shimelevich, M., Dolenko, S. (2016). Increase of the Resistance to Noise in Data for Neural Network Solution of the Inverse Problem of Magnetotellurics with Group Determination of Parameters. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_59
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