摘要
利用显微镜鉴别山羊绒是目前行之有效的方法之一,但由于类山羊绒的不断出现和山羊绒鳞片结构的变异,山羊绒与类山羊绒的鳞片鉴别特征参数相互交叉,用单独的参数相互比较易产生误判.用多维模糊C均值聚类(FCM)分析方法,将羊绒纤维鉴别特征参数按纤维直径分为7个比对(聚类)中心;按不同的权重将多维的单独参数优化成一个综合指标;通过比较综合指标与聚类中心的距离判别纤维类属.山羊绒与细羊毛及山羊绒与牦牛绒鉴别的两个实例,证明了这一分析方法,可用来验证初步判别的可信度,降低山羊绒鉴别的误判率.
There is one of effective methods to identify the cashmere by means of microscope. But the faulty identification may exist by comparing with single and individual parameter because of the emerging of similar cashmere and the scale structure variation of the cashmere, also there are some overlapping scale features between cashmere and similar cashmere. Now based on multi-dimensional fuzzy clustering means （FCM）, the characteristic parameters of cashmere identification can be grouped into 7 comparison groups （clusters）according to the fiber＇s diameter. The multi-dimensional single and individual parameter may be optimized to a combined index on the basis of the weightings. The {iber category can be identified by comparing with the combination index and the distance between clusters. Two examples： the identification between cashmere and fine wool and the identification between cashmere and yak, can prove this analytic method which may be used to verify the credibility of the initial identification, and to reduce the faulty identification possibility of cashmere.
出处
《东华大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第4期424-429,共6页
Journal of Donghua University(Natural Science)
关键词
多维
特征参数
优化
山羊绒
鉴别
mufti-dimensional
characteristic parameters
optimization
cashmere
identification
