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An Artificial Neural Network Approach to Prediction of the Colorimetric Values of the Stripped Cotton Fabrics

机译:人工神经网络方法预测棉条的比色值

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摘要

This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L* and DELTA E colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L* and DELTA E, we achieved 97 % of R for both of them. We are able to predict the L* value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of V and AE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L*.
机译:本文提出了一种基于Levenberg-Marquardt(LM)算法的人工神经网络(ANN)建模模型,用于预测使用市售活性染料染色的脱脂棉织物的比色值。在剥离工艺的应用中达到预期的效率是复制成功的一个非常重要的方面。在这项研究中,报道了通过人工神经网络对不同剥离应用的剥离棉样品的L *和DELTA E比色值的预测。为了建立最佳拟合模型,我们在隐藏层中使用不同数量的节点,输入数量和结果的MSE设置了不同的网络结构。根据预测L *和DELTA E的最佳神经网络模型的结果,我们都获得了97%的R。我们可以使用一些工作参数作为输入来预测剥离样品的L *值,而误差仅为1.2%。我们认为我们的结果非常有前景,并且可以使用研究中建立的ANN模型(尤其是对于L *)在应用任何过程之前对剥离样品的V和AE值进行预测。

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