基于BP神经网络建立二次润叶工艺参数的预测模型

doi:10.16180/j.cnki.issn1007-7820.2022.09.012

摘要/Abstract

摘要：

文中研究了在打叶复烤的二次润叶过程中,热风润叶机的工艺参数设置对出口叶片质量指标的影响,并建立相应预测模型。根据二次润叶特色工艺数据的特征,建立了BP神经网络预测模型。调用目前流行的神经网络编写框架TensorFlow的高级API接口搭架神经网络结构,逐步优化神经网络结构中的激活函数、优化器、隐藏层神经元数目等关键参数,使其对测试集的预测结果达到最佳状态。通过输入前蒸汽喷嘴压力、前端加水流量、热风温度、回风温度、进料叶片温度、进料叶片水分组合的参数,预测出口叶片水分、温度这两个关键的烟叶评测指标。根据预测结果的均方误差、均方根误差、平均绝对误差得出,当隐藏层神经元数目为7,激活函数为ReLU,优化器选择RMSprop时可取得较好的效果。

关键词: BP神经网络, 二次润叶, TensorFlow, 激活函数, 优化器, 出口叶片温度, 出口叶片水分, 均方误差

Abstract:

In this study, the influence of the process parameter setting of the hot-air leaf moisturizer on the quality index of the exit leaf during the secondary leaf conditioning of threshing and redrying is studied, and the corresponding prediction model is established. A BP neural network prediction model is established based on the characteristics of the secondary leaf conditioning process data. The current popular neural network writing framework TensorFlow's high-level API interface is called to construct the neural network structure. The activation function, optimizer, number of hidden layer neurons and other key parameters are gradually adjusted in the neural network structure to make the prediction result of the test set reach the best state. By inputting the parameters of the front steam nozzle pressure, front-end water flow rate, hot air temperature, return air temperature, feed blade temperature, and feed blade moisture combination, the two key tobacco leaf evaluation indicators, namely, outlet leaf moisture and temperature, are predicted. According to the mean square error, root mean square error, and average absolute error of the prediction results, it is concluded that when the number of neurons in the hidden layer is 7, the activation function selects ReLU, and the optimizer selects RMSprop, the effect is the best.

Key words: BP neural network, secondary leaf conditioning, TensorFlow, activation function, optimizer, exit leaf temperature, exit leaf moisture, mean square error

中图分类号:

TP399

周永长,黄亚宇. 基于BP神经网络建立二次润叶工艺参数的预测模型[J]. 电子科技, 2022, 35(9): 79-86.

ZHOU Yongchang,HUANG Yayu. Establishment of a Predictive Model of the Process Parameters of Secondary Moisturizing Based on BP Neural Network[J]. Electronic Science and Technology, 2022, 35(9): 79-86.

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编号	前蒸汽喷嘴压力 /Bar	前端加水流量 /L·h^-1	热风温度 /℃	回风温度 /℃	进料叶片温度 /℃	进料叶片水分 /%	出口叶片温度 /℃	出口叶片水分 /%
1	0.00	250.00	80.00	50.00	29.30	14.47	45.33	22.80
2	0.10	250.00	94.00	60.00	29.60	13.36	53.00	18.63
3	0.20	170.00	106.00	67.00	29.70	13.81	60.00	17.15
4	0.35	220.00	118.00	78.00	28.90	14.32	61.00	20.28
5	0.46	190.00	135.00	82.00	29.70	14.32	70.33	18.01
6	0.00	200.00	80.00	50.00	29.90	14.57	41.67	22.40
7	0.10	200.00	92.00	57.00	29.20	15.12	50.00	20.06
8	0.20	250.00	106.00	67.00	28.60	14.81	58.67	21.86
9	0.33	210.00	117.00	73.00	29.40	19.50	62.33	21.25
10	0.46	195.00	132.00	80.00	29.50	15.31	68.33	19.90

隐藏层神经元数目	MSE	RMSE	MAE
5	15.54	3.94	2.82
6	9.65	3.11	2.09
7	7.17	2.68	1.83
8	14.15	3.76	2.76
9	11.49	3.39	2.51
10	9.99	3.16	2.40
11	12.76	3.57	2.68

优化器	MSE	RMSE	MAE
RMSprop	7.17	2.68	1.83
SGD	17.83	4.22	2.81
Adagrad	38.44	6.20	4.68
Adam	12.70	3.56	2.36