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Features extraction of flotation froth images and BP neural network soft-sensor model of concentrate grade optimized
Jie-sheng Wang1, Shuang Han2, Na-na Shen2
1School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science & Technology Liaoning, Anshan 114044, China.
Abstract:
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.