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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11645 LNAI, pp. 225–234). Springer Verlag. https://doi.org/10.1007/978-3-030-26766-7_21
Mendeley helps you to discover research relevant for your work.