Ensembling neural networks: many could be better than all
Artificial Intelligence
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Basic vibration signal processing for bearing fault detection
IEEE Transactions on Education
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Load parameters inside the ball mill have direct relationships with the optimal operation of grinding process. This paper aims to develop a selective ensemble modeling approach to estimate these parameters. At first, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) adaptively. Then, frequency spectra of these IMFs are obtained via fast Fourier transform (FFT), and a serial of kernel partial least squares (KPLS) sub-models are constructed based on these frequency spectra. At last, the ensemble models are obtained by integrating the branch and band (BB) algorithm and the information entropy-based weighting algorithm. Experimental results based on a laboratory scale ball mill indicate that the propose approach not only has better prediction accuracy, but also can interpret the vibration signal more deeply.