基于FCM和BP神经网络的棉麻纤维识别方法研究

摘要

A method for cotton and bast fiber identification based on fuzzy c-means (FCM) and BP neural network is proposed.Firstly,according to the different forms of fiber transverse and longitudinal,six characteristic parameters have been extracted,then using the fuzzy c-mean algorithm,the samples has been clustered into 3 categories,and then the data clustered has been trained and forecasted as the input of BP neural network,at last the simulation experiment has been done.The experimental results show that the two combined algorithms has obvious advantages to fiber recognition,and the fiber identification method is worthy of popularization.%提出一种基于模糊c均值(FCM)和BP神经网络的棉麻纤维识别方法.首先,根据纤维横向和纵向截面形态的不同,提取6个特征参数,然后运用模糊c均值算法将样本聚类成3类,再将聚类后的数据作为BP神经网络的输入进行训练和预测,最后进行仿真实验.结果表明,将两种算法结合起来用于纤维的识别具有明显优势,是值得推广的纤维识别方法.