Shao Cheng

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In the domains of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. To solve the problems, a section identification method based on least squares support vector machines about function approximation is utilized to identify a nonlinear autoregressive external input model which is(More)
A method of tool wear intelligence measure based on Discrete Hidden Markov Models (DHMM) is proposed to monitor tool wear and to predict tool failure. FFT features are first extracted from the vibration signal and cutting force in cutting process, and then FFT vectors are presorted and converted into integers by SOM. Finally, these codes are introduced to(More)
A new method of tool wear intelligence measure based on Support Vector Machine(SVM) and Hidden Markov Models (HMM) is proposed to monitor tool wear and to predict tool failure. At first, FFT features are extracted from the model signal of the tool in cutting process, then FFT vectors are introduced to SVM-HMM for machine learning and classification. The(More)
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