Abstract
In this paper, a new approach is proposed which predicts the output of the dyeing process in textile industry by using input data consisting of the alarms and/or the interventions during the process. Back-propagation algorithms and radial basis functions are utilized to form the neural networks in predicting whether the dye process is carried out correctly or not before an operator checks it manually. Industrial data are used to test the efficiency of the proposed concept which demonstrates that the success rate is over 85%.
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