基于熵聚类模糊神经网络的轴类零件信息聚类分析

针对轴类零件，应用熵聚类模糊神经网络聚类技术进行分析．该模型利用聚类方法实现模糊输入空间划分和模糊IF-THEN规则提取，并应用梯度下降法对轴类零件的各类特征参数进行精炼．实验结果表明该方法对轴类零件的聚类分析是可行和有效的．这种方法模型比一般的模糊聚类方法更适用于数量多、类型复杂的零件分组．

Clustering Analysis of Shaft Parts Information Based on Entropy Clustering Fuzzy Neural Network

A fuzzy neural network model based on entropy clustering is applied to analyze shaft parts. The model utilizes the clustering method to partition the fuzzy input space and to extract the fuzzy IF-THEN rules. The gradient descent algorithm is used to classify and refine various characteristic parameters of shaft parts. Experimental results show that the presented method is feasible and effective to analyze shaft parts clustering, and is more adaptive than the common clustering methods to group shaft parts with many varieties and complex types.