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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)
This paper proposes an optimized algorithm for 3-D Point Based Rigid Registration. This algorithm uses an Unscented Kalman filter (UKF) for estimating the state vector of transformation, which can be interpreted as a nonlinear function of translation and rotation. In the previous work, we showed that the drawback of the UKF algorithm in estimating high(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)
An anti-surge nonlinear model predictive control system based on Least-Squared Support Vector Machine (LS-SVM) is proposed to increase the efficiency of compressor. In controller design, a compressor dynamic model is created by LS-SVM. A rolling optimization method combing genetic algorithm and LS-SVM is taken to get better real-time performance. Use the(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)
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