【摘要】 为比较BP与RBF神经网络对精纺毛纱性能的预测能力,采集前纺与后纺的工艺参数作为输入节点,表征精纺毛纱性能的条干不匀率与断裂强力分别作为输出节点,采用软件计算工具中的反向传播(BP)神经网络、径向基(RBF)神经网络分别建立细纱条干不匀率与断裂强力的预测模型,从统计学角度反映2种模型的预测性能。实验结果表明,在输入样本数较大、输入维数较高、精度要求相同的情况下,RBF神经网络模型的训练速度明显快于BP神经网络模型,但BP神经网络模型的预测性能略优于RBF函数神经网络模型,特别是遇到异常样本时,BP神经网络模型表现出更强的容错能力。更多还原

【Abstract】 A study to investigate the prediction abilities of BP and RBF neutral networks for worsted yarn performances used the process parameters collected from the fore-spinning and post-spinning as input vectors and yarn unevenness value(CV) and breaking strength(BS) indicating the worsted yarn performances as output vectors.Two software computing tools,i.e.,back-propagation(BP) neural network and radial basis function(RBF) neural network,were used to establish the prediction models for the CV and BS of the yarn respectively,and the prediction abilities of the two models were evaluated from the view point of statistics.The results show that the training speed of RBF neural network model is significantly higher than that of the BP neural network model,which are under the conditions of large-scale input samples,high input dimensions and same accuracy,but the forecasting performance of BP neural network model is slightly better than that of RBP neural network model,especially in face of abnormal sample,and BP neural network model shows better fault-tolerant capacity.更多还原