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.
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