基于BP神经网络及其改进算法的织机效率预测

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

为准确预测纺织厂织布车间的织机效率,提出利用BP神经网络、主成分分析结合BP神经网络(PCA-BP)、遗传算法改进BP神经网络(GA-BP)3种模型预测织机效率,并将GA-BP预测模型与传统BP神经网络和PCA-BP预测模型的预测结果进行对比分析。结果表明:GA-BP对原始数据的拟合度最好,相关系数为0.94687,比BP增加了6.42%,比PCA-BP增加了2.61%;GA-BP、PCA-BP、BP这3种网络十万入纬的经停仿真值与期望值间的平均误差分别为0.3412、0.3031、0.2341,误差百分率分别为8.63%、7.67%、5.92%,不同网络结构下织机效率仿真预测值与期望值间的平均误差分别为3.0109、2.6884、2.1189,误差百分率分别为3.51%、3.13%、2.47%;3种模型的预测准确度顺序由大到小为GA-BP、PCA-BP、BP。

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 0.94687,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 100000 weft insertions are 0.3412,0.3031 and 0.2341,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 3.0109,2.6884 and 2.1189,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.