论文标题
使用人工神经网络建模蘑菇生长大厅的温度变化
Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks
论文作者
论文摘要
计算机和电子系统的最新发展已将智能系统用于农业行业的自动化。在这项研究中，基于独立参数，包括环境温度，水温，新鲜空气和循环空气阻尼器和水龙头，通过多层感知器和径向基函数网络对蘑菇生长室的温度变化进行建模。根据从网络获得的结果，MLP的最佳网络是在隐藏层中的12个神经元的第二个重复，在隐藏层中的20个神经元中，用于径向基函数网络。从两个网络的比较参数获得的结果显示出最高的相关系数（0.966），最低的均方根误差（RMSE）（0.787）（0.787）和径向基函数的最低平均绝对误差（MAE）（0.02746）。因此，选择具有径向基函数的神经网络作为对蘑菇生长大厅控制系统温度的行为的预测指标。
The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.
