Abstract
Through analyzing the factors influencing BBD ball mill load and the measure characteristics, a BBD ball mill load measure method, based on rough set and neural networks information fusion, was proposed. A group of neural networks were constructed for material measure according to the relative reductions, which was feature-level information fusion. Then using the attribute significance determining method based on rough set theory, the neural networks outputs weights were determined. The weighted sums of all the neural networks outputs were regarded as decision-level information fusion. This scheme speeds up the convergence rate of the neural network learning, moreover, the redundant information is available and the robust performance of the load measure system can be improved. The simulation test shows that the proposed method can accurately reflect the BBD ball mill load, having comparatively high sensitivity.
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