Authors:
Ivan Ryzhikov
1
;
Ekaterina Nikolskaya
2
and
Yrjö Hiltunen
1
;
2
Affiliations:
1
Department of Environmental Science, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
;
2
Xamk Kuitulaboratorio, Savonlinna, Finland
Keyword(s):
Evolutionary Algorithm, Deep Learning, Parameter Estimation, Artificial Neural Network, Predictive Modeling, Nuclear Magnetic Resonance.
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 m
odel learning or optimization algorithm parameters tuning. In this study we utilized standard differential evolution algorithm and multi-layer perceptron.
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