An overview of mapping techniques for exploratory pattern analysis
Pattern Recognition
Experiments on mapping techniques for exploratory pattern analysis
Pattern Recognition
Optimal Fisher discriminant analysis using the rank decomposition
Pattern Recognition
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
A Declustering Criterion for Feature Extraction in Pattern Recognition
IEEE Transactions on Computers
Interactive Pattern Analysis and Classification
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
IEEE Transactions on Computers
Journal of Intelligent Manufacturing
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
Journal of Intelligent Manufacturing
On line tool wear monitoring based on auto associative neural network
Journal of Intelligent Manufacturing
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The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors $${\varphi_{1 }}$$ and $${\varphi_{2}}$$ . These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension.