煤矸石自适应神经元网络识别方法研究

Study on Identification Method of Self-Adaptlive Neuroid for Coal Gangue

文章对煤矸石灰分测量的特征向量进行了分析与讨论 ,通过实验选择K值与比值I J作为神经元网络的输入。基于密度加权距离的FCM神经元网络适应了不同数据对总体分布的差异性 ,在适当选择密度分布函数以后可抑制误差比较大的离群样本对神经元网络的学习的影响 ,提高了系统学习的正确率。

Analysis and discussion is made in the paper on the characteristic vector of measurement of ash content in coal gangue. Through experiment, K value and the ratio of I/J was selected as input of neuroid. Density weighted distance based FCM neuroid caters for variance of different data in general distribution and after suitable selection of density distribution function, it makes contribution to constraining the influence of stray specimen with larger error on neuroid's learning.