摘要
为有效地进行刀具状态模式识别,以端面铣刀为研究对象,采用蚁群算法对铣削力信号进行研究分析,提出一种可用于刀具状态识别的特征选择方法。该方法将特征选择过程转化成蚁群算法中蚂蚁寻找最优路径的过程,给出了转移概率公式,并运用Fisher标准判别率作为启发信息,同时将每次搜索得出的最优特征子集输入BP神经网络进行模式识别,得到的正确率整合进信息素更新策略。另外,改进了蚁群算法参数选择方法。实验结果表明,该方法可以高效地进行特征优化选择,进而使模式识别正确率较未经特征选择时得到显著提高。
In order to improve cutting tool condition pattern recognition,the face milling cutter is taken as an object and the feature of the milling force signal is analyzed using an ant colony algorithm.A method for feature selection that can be used in pattern recognition of tool wear is proposed.The method transforms the feature selection into a search for the best routes in ant colony algorithm,and the formula for this selection route is given.The fisher criterion is adopted as heuristic information.At the same time,the optimal feature subset of the current iteration cycle is put onto a BP neural network for Pattern Recognition. The precision of classification is obtained and used in the policy of pheromone update.Moreover,the method of parameter selection on the colony algorithm is improved.The experimental results show that this scheme can efficiently obtain the optimal feature subset.The accuracy is significantly higher than that obtained without feature selection.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2014年第2期372-378,403,共7页
Journal of Vibration,Measurement & Diagnosis
基金
四川省科技厅科技支撑计划资助项目(2013gz0139)
关键词
刀具
特征选择
模式识别
蚁群算法
参数
cutting tool
feature selection
pattern recognition
ant colony algorithm
parameter
