摘要
利用一种根据K－means方法对样本聚类后建立的改进型模糊神经网络(MTFNN)模型，对刀具后刀面磨损量进行在线工况实时识别。仿真结果表明该工况辨识模型精度高，收敛速度快，实用性较强，适宜于复杂的、非线性加工系统建模。
In order to identify cutting tool wear on－line,a modified type of fuzzy neural network(MTFNN)clustering in K－means method is set up.The simulation results show that the model of machining process identification has better precision,convergence and practicability.It is applicable for modeling in the complicated nonlinear machining system.
出处
《机械制造》
北大核心
2001年第5期10-12,共3页
Machinery
关键词
刀具磨损状态识别
改进型模糊神经网络
电机电信信号
Identification of Cutting Tool Wear Modified Type of Fuzzy Neural Network(MTFNN) Motor Current Signal
