Abstract
The present article discusses the process of optimizing the structure of artificial neural networks applied in modelling the wear of spheroidal graphite cast iron (SG cast iron). The networks were trained using the RPROP gradient method with the application of the SNNS package supported by original self-developed software, which enabled automatic creation, training and testing of networks with different sizes of hidden layers. Based on the results of an analysis of learning process and testing a package of 625 networks, the network was selected which – when modelling the process of spheroidal cast iron wear – generates the slightest errors during testing.
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