Abstract
In order to reduce the economic loss caused by mildew of warehousing tobacco, in this paper, particle swarm optimization algorithm is introduced into BP neural network mode. Particle swarm optimization is used to dynamically adjust the initial weights and thresholds of BP neural network. PSO-BP neural network prediction model is established to predict mildew of warehousing tobacco. Simulation experiment results show that the PSO-BP neural network model proposed in this paper is compared with the traditional BP neural network model. The prediction accuracy of warehousing tobacco mildew is higher. The effectiveness of the algorithm is verified.
Key Project of Yunnan Applied Basic Research Program (grant No. 2018FA033)
National Science Foundation of China (grant No. 61865015)
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Acknowledgment
This work is funded by grants from the National Science Foundation of China (grant No. 61865015) and by Key Project of Applied Basic Research Program of Yunnan Province (grant No. 2018FA033).
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Ye, Z., Yun, L., Li, H., Zhang, J., Wang, Y. (2019). Mildew Prediction Model of Warehousing Tobacco Based on Particle Swarm Optimization and BP Neural Network. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_21
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DOI: https://doi.org/10.1007/978-3-030-26766-7_21
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