Abstract
Due to the fact that the mill is often not working with the best conditions, it leads to increase the energy consumption of the mill system, reduce the quality of the ink produced, and greatly reduce the production efficiency. The height of the material level is a kernel factor that affects the production efficiency and quality of the mill. However, it is difficult to measure the material level accurately. In the paper, the soft measurement method is used to construct a BP neural network prediction mode, and then the material level of the mill is obtained. In addition, the BP network is easy to fall into a local optimum. In order to address this task, the genetic algorithm is used to optimize the threshold and weight of the network. MATLAB simulation is adopted to demonstrate the feasibility and effectiveness of the proposed method. The experiment results show that the production efficiency and quality of the ink can be improved by GA-BP.
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Ren, H., Zheng, S., Li, X. (2022). A Mill Control System Based on GA-BP Network for Output Prediction. In: Chu, SC., Lin, J.CW., Li, J., Pan, JS. (eds) Genetic and Evolutionary Computing. ICGEC 2021. Lecture Notes in Electrical Engineering, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-16-8430-2_8
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DOI: https://doi.org/10.1007/978-981-16-8430-2_8
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