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; 郭子豪; 汤健

doi:10.11949/0438-1157.20200673

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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

过程系统工程 | 更新时间：2026-07-24

- 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
- 化工学报 2020年71卷第12期页码：5681-5695
- 作者机构：
  
  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
- 作者简介：
  
  乔俊飞（1968—），男，教授，junfeiqiao@bjut.edu.cn
  汤健（1974—），男，教授，freeflytang@bjut.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2018YFC1900800-5);国家自然科学基金项目(62073006;61703089;61803191;61873009);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)
- DOI：10.11949/0438-1157.20200673
  中图分类号： TQ 183
- 收稿：2020-05-26，
  
  修回：2020-07-28，
  
  网络首发：2020-12-17，
  
  纸质出版：2020-12-05
- 稿件说明：
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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
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 DOI： 10.11949/0438-1157.20200673.

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 DOI： 10.11949/0438-1157.20200673.

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

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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。

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.

关键词

Keywords

references

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