使用神经网络预测膨胀烟丝的填充值
Prediction of Filling Power of Expanded Cut Tobacco with Neural Network
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摘要: 为构建叶组配方与膨胀烟丝填充值之间的关系,在工艺条件稳定的前提下,采用前馈-反向传播人工神经网络对两者间的数量关系进行了初步建模。通过选取合适的传递函数,使神经网络强大的非线性函数逼近能力得以展现,经过大数据量的训练后,该模型具备了良好的预测能力,预测结果的相对误差为5%左右。Abstract: In order to find the relationship between the composition of tobacco leaf and the filling power of expanded cut tobacco,the quantitative relation between them was preliminarily modeled under steady processing parameters by feed-forward back-propagation artificial neural network.The powerful ability of nonlinear function approximation of neural network was demonstrated by choosing suitable transfer function.After training with abundant data,the model possessed good prediction ability and the relative error of predictive results was around 5%.
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