化工学报 ›› 2020, Vol. 71 ›› Issue (5): 2128-2138.DOI: 10.11949/0438-1157.20191378
收稿日期:
2019-11-13
修回日期:
2020-01-18
出版日期:
2020-05-05
发布日期:
2020-05-05
通讯作者:
刘乙奇
作者简介:
李东(1994—),男,博士研究生,基金资助:
Dong LI(),Daoping HUANG,Yiqi LIU()
Received:
2019-11-13
Revised:
2020-01-18
Online:
2020-05-05
Published:
2020-05-05
Contact:
Yiqi LIU
摘要:
软测量技术被广泛应用到工业过程中重要且难以在线测量变量的预测。然而,由于工业过程的复杂性,非线性和高昂的数据获取成本,使得建模所需的输入和输出变量数据比例严重不平衡。因此,本文在已有的co-training模型的基础上,将协同训练算法与前馈神经网络(BP)算法相结合,提出了针对非线性问题的co-training BP模型。然而,由于软测量模型应用过程的时变性和不确定性,以及外部环境等因素的影响,会造成数据突变、延迟和波动性大等情况,导致模型预测性能的衰减。因此,本文提出了一种半监督异构的自适应co-training RPLS-RBP模型。一方面,该模型使用奇偶分组的方法将标记数据进行两部分均分。另一方面,递归PLS(RPLS)与递归BP(RBP)同时用于标记数据的建模和预测。为了验证模型的预测性能,所提出模型在一个污水处理的仿真基准平台(BSM1)和一个实际污水厂(UCI)的数据中得到了验证。结果表明,所提模型具有较好的预测性能。
中图分类号:
李东, 黄道平, 刘乙奇. 基于协同训练的半监督异构自适应软测量建模方法的研究[J]. 化工学报, 2020, 71(5): 2128-2138.
Dong LI, Daoping HUANG, Yiqi LIU. Research on semi-supervised heterogeneous adaptive co-training soft-sensor model[J]. CIESC Journal, 2020, 71(5): 2128-2138.
异构自适应co-training RPLS-RBP混合回归模型流程 |
---|
输入: 标记样本集 训练过程: 用奇偶分配的方法将 进行 For 取最大的 End of for 达到最高迭代次数,结束迭代 输出新的标记样本集L1和L2 |
表1 异构自适应co-training RPLS-RBP混合回归模型的详细流程
Table 1 Detailed flow of heterogeneous adaptive co-training RPLS-RBP hybrid regression model
异构自适应co-training RPLS-RBP混合回归模型流程 |
---|
输入: 标记样本集 训练过程: 用奇偶分配的方法将 进行 For 取最大的 End of for 达到最高迭代次数,结束迭代 输出新的标记样本集L1和L2 |
Labeled data rate | co-training PLS | co-training BP | co-training RRPLS-RBP | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SS | SNH | SNO | COD | BOD5 | SS | SNH | SNO | COD | BOD5 | SS | SNH | SNO | COD | BOD5 | |
10% | 0.095 | 0.053 | 0.054 | 0.064 | 0.053 | 0.304 | 0.07 | 0.027 | 0.089 | 0.029 | 0.007 | 0.004 | 0.003 | 0.004 | 0.004 |
20% | 3.833 | 12.994 | 8.345 | 2.456 | 1.922 | 16.845 | 10.073 | 8.328 | 3.293 | 1.615 | 0.974 | 0.661 | 0.638 | 0.69 | 0.587 |
30% | 2.066 | 1.527 | 0.892 | 0.801 | 0.751 | 7.227 | 2.457 | 2.4 | 0.741 | 0.421 | 0.311 | 0.174 | 0.174 | 0.18 | 0.182 |
40% | 16.864 | 10.756 | 12.205 | 7.173 | 6.059 | 54.485 | 11.066 | 2.182 | 1.432 | 6.136 | 0.263 | 0.293 | 0.252 | 0.32 | 0.238 |
50% | 0.043 | 0.088 | 0.04 | 0.037 | 0.044 | 0.207 | 0.055 | 0.037 | 0.037 | 0.052 | 0.003 | 0.005 | 0.002 | 0.005 | 0.003 |
表2 不同的标记样本率下的RMSE值
Table 2 RMSE values at different labeled data rate
Labeled data rate | co-training PLS | co-training BP | co-training RRPLS-RBP | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SS | SNH | SNO | COD | BOD5 | SS | SNH | SNO | COD | BOD5 | SS | SNH | SNO | COD | BOD5 | |
10% | 0.095 | 0.053 | 0.054 | 0.064 | 0.053 | 0.304 | 0.07 | 0.027 | 0.089 | 0.029 | 0.007 | 0.004 | 0.003 | 0.004 | 0.004 |
20% | 3.833 | 12.994 | 8.345 | 2.456 | 1.922 | 16.845 | 10.073 | 8.328 | 3.293 | 1.615 | 0.974 | 0.661 | 0.638 | 0.69 | 0.587 |
30% | 2.066 | 1.527 | 0.892 | 0.801 | 0.751 | 7.227 | 2.457 | 2.4 | 0.741 | 0.421 | 0.311 | 0.174 | 0.174 | 0.18 | 0.182 |
40% | 16.864 | 10.756 | 12.205 | 7.173 | 6.059 | 54.485 | 11.066 | 2.182 | 1.432 | 6.136 | 0.263 | 0.293 | 0.252 | 0.32 | 0.238 |
50% | 0.043 | 0.088 | 0.04 | 0.037 | 0.044 | 0.207 | 0.055 | 0.037 | 0.037 | 0.052 | 0.003 | 0.005 | 0.002 | 0.005 | 0.003 |
预测变量 | co-training PLS | co-training BP | co-training RPLS-RBP | |||
---|---|---|---|---|---|---|
RMSE | R | RMSE | R | RMSE | R | |
SS | 0.053 | 0.814 | 0.029 | 0.829 | 0.004 | 0.941 |
SNH | 1.922 | 0.922 | 1.615 | 0.921 | 0.587 | 0.968 |
SNO | 0.751 | 0.917 | 0.421 | 0.935 | 0.182 | 0.973 |
COD | 6.059 | 0.947 | 6.136 | 0.753 | 0.238 | 0.979 |
BOD5 | 0.044 | 0.987 | 0.052 | 0.966 | 0.003 | 0.991 |
耗时/s | 34.368 | 58.994 | 182.318 |
表3 输出变量的RMSE、R值和耗时(标记样本率为50%)
Table 3 RMSE, R values and time consuming of the output variables (labeled data rate is 50%)
预测变量 | co-training PLS | co-training BP | co-training RPLS-RBP | |||
---|---|---|---|---|---|---|
RMSE | R | RMSE | R | RMSE | R | |
SS | 0.053 | 0.814 | 0.029 | 0.829 | 0.004 | 0.941 |
SNH | 1.922 | 0.922 | 1.615 | 0.921 | 0.587 | 0.968 |
SNO | 0.751 | 0.917 | 0.421 | 0.935 | 0.182 | 0.973 |
COD | 6.059 | 0.947 | 6.136 | 0.753 | 0.238 | 0.979 |
BOD5 | 0.044 | 0.987 | 0.052 | 0.966 | 0.003 | 0.991 |
耗时/s | 34.368 | 58.994 | 182.318 |
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