Author:

Keywords:

neural networks, self-organizing maps, spreading pattern, centrifugal spreader, spinning disc spreader, prediction, classification, machine settings, physical properties, fertilizer particles, 01 Mathematical Sciences, 02 Physical Sciences, 08 Information and Computing Sciences, Numerical & Computational Mathematics, 46 Information and computing sciences, 49 Mathematical sciences, 51 Physical sciences

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. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.