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ORIGINAL PAPER
Neuronal modelling of selected agricultural objects with usage of Johan Gielis's supershape
Journal of Research and Applications in Agricultural Engineering 2008;53(1):22-25
KEYWORDS
ABSTRACT
The aim of the work was to study the classifying possibilities of neural networks in the identification process of the wheat's, barley's and corn's kernel. Applied separation method depended on recognizing the shape differences of analysed objects. In order to identify the shape, and afterwards to encode the obtained empirical data into the training data sets the Johan Gielis's supershape formula was used. This formula permits for projection of any shape with a help of six independent parameters.
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| eISSN: | 2719-423X |
| ISSN: | 1642-686X |
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