Open AccessPeculiarities of regression model design based on neural networks
Author(s) -
N. Yu. Grigoryeva,
A. S. Perkov,
T. R. Zhangirov,
A. A. Liss
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1658/1/012020
Subject(s) - artificial neural network , dropout (neural networks) , regression , standard deviation , sample (material) , computer science , linear regression , artificial intelligence , regression analysis , statistics , machine learning , mathematics , chemistry , chromatography
In this article a neural-network regression model for prediction of the bacterioplankton abundance according to physicochemical parameters of the environmental conditions is considered and some of the peculiarities of its development are described. A particular case of small and very heterogeneous data sample, typical for biological applications, is analysed. To solve this problem, a number of multi-layer feed-forward neural networks with different architectures are studied. The regression results are estimated on the base of determination coefficient and standard deviation of predicted values in the test sample. The effect of the dropout, applied to one of the hidden layers, on learning process and obtained results is analysed.