Estimation of longitudinal precipitation of liquid indicator (LPLI) with the use of the artificial neural network (MLP, RBF) models
Journal Title: Journal of Research and Applications in Agricultural Engineering (ISSN 1642-686X) - Year 2018, Vol 63, Issue 1
Abstract
The study presents the results of the analysis of two artificial neural networks as models of relationships between longitudinal precipitation of liquid indicator and selected technical and technological factors of spraying process. The measurements were conducted in laboratory conditions. A wind tunnel was primary element in experimental set-up. Based on the results, it can be stated that MLP model (R2 = 0.908 for validation data set) was more accurate that RBF model (R2 = 0.837 for validation data set). The analysis of input variables’ contribution indicated that the LPLI is influenced the most by the air flow speed and the droplet size. Spray boom height and spray nozzle angle were less influencing parameters.
Authors and Affiliations
Katarzyna Pentoś, Beata Cieniawska, Deta Łuczycka
Keywords
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- EP ID EP520817
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