Combining Deep Learning Model and Evolutionary Optimization for Parameters Identification of NMR Signal

Ivan Ryzhikov, Ekaterina Nikolskaya, Yrjö Hiltunen, Yrjö Hiltunen

2022

Abstract

In this study we combine deep learning predictive models and evolutionary optimization algorithm to solve parameter identification problem. We consider parameter identification problem coming from nuclear magnetic resonance signals. We use observation data of sludges and solving water content analysis problem. The content of the liquid flow is the basis of production control of sludge dewatering in various industries. Increasing control performance brings significant economic effect. Since we know the mathematical model of the signal, we reduce content analysis problem to optimization problem and parameters estimation problem. We investigate these approaches and propose a combined approach, which involves predictive models in initial optimization alternative set generation. In numerical research we prove that proposed approach outperforms separate optimization-based approach and predictive models. In examination part, we test approach on signals that were not involved in predictive model learning or optimization algorithm parameters tuning. In this study we utilized standard differential evolution algorithm and multi-layer perceptron.

Paper Citation

in Harvard Style

Ryzhikov I., Nikolskaya E. and Hiltunen Y. (2022). Combining Deep Learning Model and Evolutionary Optimization for Parameters Identification of NMR Signal. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 761-768. DOI: 10.5220/0011004200003122

in Bibtex Style

@conference{icpram22,
author={Ivan Ryzhikov and Ekaterina Nikolskaya and Yrjö Hiltunen},
title={Combining Deep Learning Model and Evolutionary Optimization for Parameters Identification of NMR Signal},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={761-768},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011004200003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Combining Deep Learning Model and Evolutionary Optimization for Parameters Identification of NMR Signal
SN - 978-989-758-549-4
AU - Ryzhikov I.
AU - Nikolskaya E.
AU - Hiltunen Y.
PY - 2022
SP - 761
EP - 768
DO - 10.5220/0011004200003122