Application of Bidirectional LSTM Neural Network in Grain Stack Temperature Prediction

Abstract

AbstractFood is a necessity for human survival, so it is particularly important to ensure the quality of food in the process of storage. Therefore, in the process of grain storage, the grain quality is extremely important. In the process of grain storage, the temperature of grain will change due to the accumulation of heat in the grain pile, which will eventually affect the quality of grain and thus affect the quality of grain. Therefore, it is necessary to accurately predict the temperature of grain pile. Based on this practical problem, this paper designs a bidirectional LSTM neural network structure, and trains and learns the existing temperature data through the neural network, so as to accurately predict the temperature at a certain time. The prediction results are compared with those of LSTM neural network. At the end of the experiment, a comprehensive comparative analysis is conducted with RNN neural network, bi-directional RNN neural network, GRU neural network and bi-directional GRU neural network. The experimental results show that the bi-directional LSTM neural network has a better temperature change trend than the LSTM neural network, RNN neural network, bi-directional RNN neural network, GRU neural network, bidirectional GRU neural network has better predictive ability. In this article the bidirectional LSTM neural network to predict the temperature of the grain heap experiments, so it can be applied to the actual process of grain storage, can through the grain heap more precise prediction of temperature, so the grain heap temperature can be precise adjustment, it ensures the quality of grain, which is of great significance for grain storage. KeywordsBidirectional LSTM neural networkLSTM neural networkTemperature predictionNetwork training