基于改进大趋势扩散和隐含层插值的虚拟样本生成方法及应用

doi:10.11949/0438-1157.20200673

化工学报 ›› 2020, Vol. 71 ›› Issue (12): 5681-5695.DOI: 10.11949/0438-1157.20200673

基于改进大趋势扩散和隐含层插值的虚拟样本生成方法及应用

乔俊飞^1,²(),郭子豪^1,^2,³,汤健^1,²()

^1.北京工业大学信息学部，北京 100124
^2.计算智能与智能系统北京市重点实验室，北京 100124
^3.北京轨道交通技术装备集团，北京 100071

收稿日期:2020-05-26 修回日期:2020-07-28 出版日期:2020-12-05 发布日期:2020-12-05
通讯作者: 汤健
作者简介:乔俊飞（1968—），男，教授，junfeiqiao@bjut.edu.cn
基金资助:
国家重点研发计划项目(2018YFC1900800-5);国家自然科学基金项目(62073006);矿冶过程自动控制技术国家重点实验室，矿冶过程自动控制技术北京市重点实验室项目(BGRIMM-KZSKL-2020-02)

Virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation with its application

QIAO Junfei^1,²(),GUO Zihao^1,^2,³,TANG Jian^1,²()

^1.Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
^2.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
^3.Beijing Rail Transit Technology Equipment Group Co. , Ltd. , Beijing 100071，China

Received:2020-05-26 Revised:2020-07-28 Online:2020-12-05 Published:2020-12-05
Contact: TANG Jian

摘要/Abstract

摘要：

针对获取复杂工业过程的难以检测质量或环境污染指标数据的时间和经济成本高导致有标记建模样本稀缺的问题，提出了基于改进大趋势扩散和隐含层插值的虚拟样本生成（VSG）方法，并将其应用于城市固废焚烧过程的二英（DXN）排放预测。首先，采用基于子区域欧氏距离改进大趋势扩散（MTD）方法对真实样本输入/输出空间进行扩展；接着，采用等间隔插值方式生成虚拟样本输入，再结合映射模型和删减机制获得虚拟样本输出；然后，采用基于正则化改进的随机权神经网络隐含层插值依次得到虚拟样本输出和输入，再结合扩展空间对虚拟样本进行删减；最后，将上述具有互补性的虚拟样本与原始真实样本进行混合，实现建模数据容量扩充。通过基准数据集和工业过程DXN数据验证了所提方法的有效性和合理性。

关键词: 大趋势扩散, 神经网络隐含层插值, 虚拟样本生成, 二英排放预测, 废物处理, 算法, 模型

Abstract:

The time and economic cost of obtaining difficult-to-measure quality or environmental pollution indices data for complex industrial processes are very high, which leads to the scarcity of labeled modeling samples. Aimed at this problem, a new virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation is proposed. It is applied to the dioxin (DXN) emissions prediction of municipal solid waste cineration process. First, the true sample input/output sample space is expanded by using improved megatrend diffusion technology (MTD) based on the sub-regional Euclidean distance. Next, the virtual sample input is generated using an equal interval interpolation method, and the virtual sample output is obtained by combining the mapping model and the pruning mechanism. Then, the hidden layer interpolation method based on the improved random weight neural network with regularization mechanism is used to obtain the virtual sample output and input, and the virtual sample is deleted by combining with the expansion space. Finally, the above-mentioned complementary input/output virtual samples are mixed with the original true samples to realize the expansion of the modeling data capacity. The validity and rationality of the proposed method are verified by benchmark data set and industrial process DXN data.

Key words: megatrend diffusion, neural network hidden layer interpolation, virtual sample generation, dioxin emission prediction, waste treatment, algorithm, model

中图分类号:

TQ 183

乔俊飞,郭子豪,汤健. 基于改进大趋势扩散和隐含层插值的虚拟样本生成方法及应用[J]. 化工学报, 2020, 71(12): 5681-5695.

QIAO Junfei,GUO Zihao,TANG Jian. Virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation with its application[J]. CIESC Journal, 2020, 71(12): 5681-5695.

图/表 22

图1 真实样本与期望样本空间的关系

Fig.1 Relationship between expected sample space and real sample space

图2 所提VSG方法的策略

Fig.2 Strategy of the proposed VSG method

图3 基准数据原始小样本空间与扩展空间的对比

Fig.3 Comparison between original small sample space and extended space

表1 基准数据扩展输出空间与原始输出空间的对比

Table 1 Comparison of extended output space and original output space for Benchmark data

项目	混凝土抗压强度/MPa
y_max	79.99
y_vsg-max	100.472
上限扩展率	25.61%
y_min	6.88
y_vsg-min	0
下限扩展率	100%

表2 基准数据等间隔策略生成虚拟样本的输入/输出（以n=3时第1和2组样本为例）

Table 2 Input and output of virtual samples generated based on equal interval method for Benchmark data (taking the first and second samples with n=3 as example)

样本	浓度/(kg/m³)							龄期/d	混凝土抗压强度/MPa
样本	水泥	高炉渣	粉煤灰	水	超塑化剂	粗集料	细集料	龄期/d	混凝土抗压强度/MPa
第1组数据	540	0	0	162	2.5	1040	676	28	37.104
第2组数据	332.5	142.5	0	228	0	932	594	180	30.674
第1组生成样本	488.125	35.625	0	178.5	1.875	1013	655.5	66	39.207
第2组生成样本	436.25	71.25	0	195	1.25	986	635	104	41.31
第3组生成样本	384.375	106.875	0	211.5	0.625	959	614.5	142	35.992
备注	样本输入	样本输入	样本输入	样本输入	样本输入	样本输入	样本输入	样本输入	样本输出

图4 基准数据样本输出的删减数量与生成数量的关系

Fig.4 Relationship between the deleted number and the generated number of the virtual sample output for Benchmark data

表3 基准数据未加入正则化项基于多组隐含层插值法的虚拟样本输入/输出结果（以前5组为例）

Table 3 Virtual sample input and output results based on multiple sets of hidden layer interpolation method without regularization term for Benchmark data (taking the former five samples as example)

n	X_insert-temp								Y_insert-temp
1	292.664	70.481	-69.88	175.089	2.292	802.293	543.142	-35.603	44.01
2	278.301	93.42	-49.115	181.486	4.993	819.112	511.977	-62.01	44.053
3	250.453	99.612	-31.037	187.579	5.812	802.988	520.632	-79.17	41.626
4	233.829	75.385	-36.606	185.512	3.208	763.418	569.621	-64.189	38.377
5	103.508	-17.664	-29.344	153.978	-2.336	735.579	592.56	-21.546	41.288
合格比率	100%	80%	0%	100%	80%	100%	80%	0%	100%

表4 基准数据加入正则化项基于多组隐含层插值法的虚拟输入/输出结果（以前5组样本为例）

Table 4 Virtual sample input and output results based on multiple sets of hidden layer interpolation method with adding regularization term for Benchmark data (taking the former five samples as example)

n	X_insert-temp								Y_insert-temp
1	583.657	271.588	197.941	234.288	18.838	1172.991	934.768	201.621	35.034
2	584.102	271.682	197.839	234.446	18.824	1172.598	934.191	201.648	34.975
3	584.548	271.775	197.736	234.604	18.809	1172.206	933.614	201.674	34.915
4	584.994	271.869	197.633	234.762	18.795	1171.813	933.037	201.7	34.856
5	585.439	271.962	197.53	234.921	18.78	1171.42	932.46	201.727	34.796
合格比率	100%	100%	100%	100%	100%	100%	100%	100%	100%

图5 基准数据正则化项对虚拟样本合格率的影响

Fig.5 Effect of regularization term on sample qualification rate

表5 基准数据删减后的基于多组隐含层插值法的虚拟样本输入/输出结果（以前5组样本为例）

Table 5 Virtual sample input and output results based on multiple sets of hidden layer interpolation method with deletion operation for Benchmark data (taking the former five samples as example)

n	X_insert								Y_insert
1	583.657	271.588	197.941	234.288	18.838	1172.991	934.768	201.621	35.034
2	584.102	271.682	197.839	234.446	18.824	1172.598	934.191	201.648	34.975
3	584.548	271.775	197.736	234.604	18.809	1172.206	933.614	201.674	34.915
4	584.994	271.869	197.633	234.762	18.795	1171.813	933.037	201.7	34.856
5	585.439	271.962	197.53	234.921	18.78	1171.42	932.46	201.727	34.796

表6 基准数据实验结果比较

Table 6 Comparison of experimental results for Benchmark data

实验	虚拟样本数量	RMSE均值	RMSE方差	最佳RMSE及插值参数
实验	虚拟样本数量	RMSE均值	RMSE方差	数值	$n e q u a l$	$n i n s e r t$	$n l a y e r$
A	0	23.599	13.226	—	—	—	—
B	60	18.370	11.330	16.577	6	—	—
C	161	16.156	8.831	14.525	3	—	6
D	29	21.128	112.204	15.498	1	1	3
E	32	19.780	93.273	14.466	2	1	3
F	729	16.134	9.692	13.383	6	2	8

表6 基准数据实验结果比较

Table 6 Comparison of experimental results for Benchmark data

实验	虚拟样本数量	RMSE均值	RMSE方差	最佳RMSE及插值参数
实验	虚拟样本数量	RMSE均值	RMSE方差	数值	$n e q u a l$	$n i n s e r t$	$n l a y e r$
A	0	23.599	13.226	—	—	—	—
B	60	18.370	11.330	16.577	6	—	—
C	161	16.156	8.831	14.525	3	—	6
D	29	21.128	112.204	15.498	1	1	3
E	32	19.780	93.273	14.466	2	1	3
F	729	16.134	9.692	13.383	6	2	8

图6 基准数据不同方法的测试样本预测曲线

Fig.6 Prediction curves of test samples with different methods of Benchmark data

图7 典型基于炉排炉的MSWI工艺流程图

Fig.7 Typical MSWI process flow chart based on grate furnace

图8 DXN数据原始小样本空间与扩展空间对比（以前5组特征变量为例）

Fig.8 Comparison between original small sample space and extended space (taking the former 5 input features as example) for DXN data

表7 DXN数据扩展输出空间与原始输出空间的对比

Table 7 Comparison between extended output space and original output space for DXN data

项目	DXN浓度/(ng TEQ/m³)
y_max	0.083
y_vsg-max	0.133
上限扩展率	60.24%
y_min	0.002
y_vsg-min	0
下限扩展率	100%

表8 DXN数据基于等间隔法生成的虚拟样本输入/输出结果（以n=3时第1和2组样本为例）

Table 8 Virtual samples input and output based on generated at equal interval method (taking the first and second samples with n=3 as example) for DXN data

样本	反应器入口氧气浓度/%	燃烧炉排右空气流量×10^-3/(m³/h)	二次空预器出口温度/℃	干燥炉排入口空气温度/℃	燃烧炉排左侧温度/℃	DXN浓度/ (ng TEQ/m³)
第1组数据	4.8	1.5	14	176	181	0.05
第2组数据	3.2	3.4	24	180	198	0.035
第1组生成样本	12.054	5.184	33.801	209.677	257.477	0.036
第2组生成样本	12.244	5.261	34.361	211.212	259.972	0.0362
第3组生成样本	12.435	5.338	34.921	212.748	262.468	0.0364
备注	样本输入(DXN变量1)	样本输入(DXN变量2)	样本输入(DXN变量3)	样本输入(DXN变量4)	样本输入(DXN变量5)	样本输出

图9 DXN数据样本输出的删减数量与生成数量的关系

Fig.9 Relationship between the number of deletion and generation of virtual sample output for DXN data

表9 DXN数据未加入正则化项基于多组隐含层插值法的输入/输出结果（以前5组样本为例）

Table 9 Input and output results based on multiple groups of hidden layer interpolation method without regularization term for DXN data (taking the former five samples as example)

n	X_insert-temp					Y_insert-temp
1	-1.839	-0.766	4.944	84.585	90.561	0.0417
2	-1.862	-0.749	4.963	84.512	90.128	0.0413
3	-1.885	-0.731	4.982	84.439	89.695	0.0409
4	-1.908	-0.714	5	84.365	89.263	0.0405
5	-1.931	-0.697	5.019	84.292	88.83	0.0401
合格比率	100%	0%	100%	100%	100%	100%

表10 DXN数据加入正则化项基于多组隐含层插值法的输入/输出结果（以前5组样本为例）

Table 10 Virtual sample input and output results based on multiple sets of hidden layer interpolation method with adding regularization term for DXN data (taking the former five samples as example)

n	X_insert-temp					Y_insert-temp
1	12.584	5.416	33.933	214.927	262.392	0.0498
2	12.603	5.431	33.986	214.748	262.18	0.0493
3	12.622	5.446	34.039	214.568	261.969	0.0488
4	12.641	5.461	34.091	214.388	261.757	0.0483
5	12.66	5.476	34.144	214.209	261.545	0.0478
合格比率	100%	100%	100%	100%	100%	100%

图10 DXN数据正则化项对样本合格率的影响

Fig.10 Effect of regularization term on sample qualification rate for DXN data

表11 DXN数据删减后的基于多组隐含层插值法的虚拟样本输入/输出结果（以前5组样本为例）

Table 11 Virtual sample input and output results based on multiple sets of hidden layer interpolation method with deletion operation for DXN data (taking the former five samples as example)

n	X_insert					Y_insert
1	12.584	5.416	33.933	214.927	262.392	0.0498
2	12.603	5.431	33.986	214.748	262.18	0.0493
3	12.622	5.446	34.039	214.568	261.969	0.0488
4	12.641	5.461	34.091	214.388	261.757	0.0483
5	12.66	5.476	34.144	214.209	261.545	0.0478

表12 DXN数据实验结果比较

Table 12 Comparison of experimental results for DXN data

实验	虚拟样本数量	RMSE均值	RMSE方差	最佳RMSE及插值参数
实验	虚拟样本数量	RMSE均值	RMSE方差	数值	$n e q u a l$	$n i n s e r t$	$n l a y e r$
A	0	0.0383	0.000203	—	—	—
B	136	0.0440	0.000220	0.0379	8
C	61	0.0304	1.108×10^-5	14.525	2	—	2
D	436	0.0288	2.532×10^-5	0.0255	2	9	3
E	81	0.0289	2.073×10^-5	0.0255	1	2	6
F	501	0.0286	1.697×10^-5	0.0254	5	1	7

表12 DXN数据实验结果比较

Table 12 Comparison of experimental results for DXN data

实验	虚拟样本数量	RMSE均值	RMSE方差	最佳RMSE及插值参数
实验	虚拟样本数量	RMSE均值	RMSE方差	数值	$n e q u a l$	$n i n s e r t$	$n l a y e r$
A	0	0.0383	0.000203	—	—	—
B	136	0.0440	0.000220	0.0379	8
C	61	0.0304	1.108×10^-5	14.525	2	—	2
D	436	0.0288	2.532×10^-5	0.0255	2	9	3
E	81	0.0289	2.073×10^-5	0.0255	1	2	6
F	501	0.0286	1.697×10^-5	0.0254	5	1	7

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基于改进大趋势扩散和隐含层插值的虚拟样本生成方法及应用

Virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation with its application

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