Prediction of spreading processes using a supervised Self-Organizing Map

Dimitrios Moshou, Koen Deprez and Herman Ramon

Mathematics and Computers in Simulation (MATCOM), 2004, vol. 65, issue 1, 77-85

Abstract: A novel technique is presented based on self-organizing neural networks for prediction of fertilizer distribution patterns of spreaders as a function of spreader settings and fertilizer properties. The main aim of the presented technique is to predict tendencies in the spreading distribution pattern as a function of machine configurations and physical fertilizer properties. The Self-Organizing Map is used in a novel way to represent input–output relationships between high-dimensional spaces. Other NN methods would be very difficult to use because of the high dimensions of the input and output spaces. In the case of a multilayer perceptron, the global connectivity would lead to a prohibitively large number of free parameters giving rise to learning time problems. The spreading distribution patterns are predicted with a high performance with the proposed technique.

Keywords: Neural networks; Self-Organizing Maps; Spreading pattern; Centrifugal spreader; Spinning disc spreader; Prediction; Classification; Machine settings; Physical properties; Fertilizer particles (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:65:y:2004:i:1:p:77-85

DOI: 10.1016/j.matcom.2003.09.010

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