小样本纱线质量预测的机器学习算法适用性分析

Applicability analyses of machine learning algorithm for yarn quality prediction under small samples conditions

为了解决当前基于神经网络的纱线质量预测模型针对小样本预测精度偏低和预测精度不稳定的问题,建立了随机森林(RF)算法预测模型、多层感知机神经网络(MLP)算法预测模型和线性回归(LR)算法预测模型,就各算法模型在小样本情况下对不同数据特点的数据集的敏感性、不同数据维度的敏感性和不同训练样本数的敏感性进行了预测性能对比试验。用决定系数和均方根误差进行模型预测性能评估。试验结果表明:在小样本情况下,相比于MLP算法和LR算法,大多数情况下RF算法预测准确性更高、预测精度稳定性更好、对小训练样本量的适应性更好,具有较高的综合预测性能。

In order to solve the problems of low and unstable prediction accuracy of the current yarn quality prediction model based on neural network,three prediction models were established,including RF(random forest)algorithm prediction model,MLP(multi-layer perceptron)neural network algorithm prediction model and LR(linear regression)algorithm prediction model.Experiments were conducted to compare the three models′prediction performance on the sensitivity with small samples in different data distribution,the sensitivity in different data dimensions and the sensitivity in different training sample sizes.The coefficient of determination and root mean square error were used to evaluate the prediction performance of each model.The experimental results showed that,compared with MLP algorithm and LR algorithm,in most cases,RF algorithm had higher prediction accuracy,better stability of prediction accuracy,better adaptability to small training sample size,and higher comprehensive prediction performance.