Prediction of loom efficiency based on BP neural network and its improved algorithm

In order to predict the loom efficiency more accurately in the weaving workshop of textile mills, three models, i.e. BP neural network, principal component analysis combined with BP neural network(PCA-BP) and genetic algorithm modified BP neural network model (GA-BP), were used to predict the loom efficiency. At the same time, the prediction results of the GA-BP were compared with that of the BP neural network and PCA-BP neural network. The results show that the GA-BP has the best fitting degree to the original data, the correlation coefficient is 0.946 87, which is 6.42% higher than BP and 2.61% higher than PCA-BP. The average absolute errors between the simulated output value and the expected loom stoppage values over 100 000 weft insertions are 0.341 2, 0.303 1 and 0.234 1, respectively, for GA-BP, PCA-BP and BP models, corresponding to error percentages 8.63%, 7.67% and 5.92%. The average errors between the predicted and the expected values of the loom efficiency with different network models are 3.010 9, 2.688 4 and 2.118 9, respectively, with error percentages of 3.51%, 3.13%, 2.47%. The order of prediction accuracy of the three models is GA-BP, PCA-BP and BP. Copyright No content may be reproduced or abridged without authorization.