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为实现对转轮除湿系统出口温度、含湿量的高精度预测,基于对复杂系统建模具有良好适应性和稳定性的Elman神经网络,通过正交实验采集神经网络训练数据,根据神经网络建模过程建立了转轮除湿系统的模型,实现了对除湿系统出口空气状态的预测。最后,对BP和Elman神经网络建模效果进行了对比,结果表明:Elman模型具有更高的预测精度和更好的稳定性。
In order to achieve the high-precision prediction of exit temperature and moisture content of rotary dehumidification system, based on the Elman neural network with good adaptability and stability to complex system modeling, neural network training data were acquired through orthogonal experiment. According to neural network The model establishes the model of the rotor dehumidification system and realizes the prediction of the outlet air state of the dehumidification system. Finally, the modeling results of BP and Elman neural networks are compared. The results show that Elman model has higher prediction accuracy and better stability.