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面向复杂工业过程的虚拟样本生成综述

汤健 崔璨麟 夏恒 乔俊飞

汤健, 崔璨麟, 夏恒, 乔俊飞. 面向复杂工业过程的虚拟样本生成综述. 自动化学报, 2024, 50(1): 1−31 doi: 10.16383/j.aas.c221006
引用本文: 汤健, 崔璨麟, 夏恒, 乔俊飞. 面向复杂工业过程的虚拟样本生成综述. 自动化学报, 2024, 50(1): 1−31 doi: 10.16383/j.aas.c221006
Tang Jian, Cui Can-Lin, Xia Heng, Qiao Jun-Fei. A survey of virtual sample generation for complex industrial processes. Acta Automatica Sinica, 2024, 50(1): 1−31 doi: 10.16383/j.aas.c221006
Citation: Tang Jian, Cui Can-Lin, Xia Heng, Qiao Jun-Fei. A survey of virtual sample generation for complex industrial processes. Acta Automatica Sinica, 2024, 50(1): 1−31 doi: 10.16383/j.aas.c221006

面向复杂工业过程的虚拟样本生成综述

doi: 10.16383/j.aas.c221006
基金项目: 国家自然科学基金(62073006, 62173120), 北京市自然科学基金(4212032), 科技创新2030-“新一代人工智能” 重大项目(2021ZD0112301, 2021ZD0112302)资助
详细信息
    作者简介:

    汤健:北京工业大学信息学部教授. 主要研究方向为小样本数据建模, 城市固废处理过程智能控制. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

    崔璨麟:北京工业大学信息学部硕士研究生. 主要研究方向为城市固废焚烧过程风险预警, 虚拟样本生成. E-mail: cuicanlin@emails.bjut.edu.cn

    夏恒:北京工业大学信息学部博士研究生. 主要研究方向为树结构深/宽度学习结构设计与优化, 城市固废焚烧过程二噁英排放预测. E-mail: xiaheng@emails.bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为污水处理过程智能控制, 神经网络结构设计与优化. E-mail: junfeiq@bjut.edu.cn

A Survey of Virtual Sample Generation for Complex Industrial Processes

Funds: Supported by National Natural Science Foundation of China (62073006, 62173120), Beijing Natural Science Foundation (4212032) and National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302)
More Information
    Author Bio:

    TANG Jian Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process. Corresponding author of this paper

    CUI Can-Lin Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers risk warning of municipal solid waste incineration process and virtual sample generation

    XIA Heng Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers structure design and optimization of tree-structured deep/broad learning and dioxin emission prediction of the municipal solid waste incineration process

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control of wastewater treatment process, and structure design and optimization of neural networks

  • 摘要: 用于复杂工业过程难测运行指标和异常故障建模的样本具有量少稀缺、分布不均衡以及内涵机理知识匮乏等特性. 虚拟样本生成(Virtual sample generation, VSG)作为扩充建模样本数量及其涵盖空间的技术, 已成为解决上述问题的主要手段之一, 但已有研究还存在缺乏理论支撑、分类准则与应用边界模糊等问题. 本文在描述复杂工业过程难测运行指标和异常故障建模所存在问题的基础上, 梳理虚拟样本定义及其内涵, 给出面向工业过程回归与分类问题的VSG实现流程; 接着, 从样本覆盖区域、实现流程与推广应用等方向进行综述; 然后, 分析讨论VSG的下一步研究方向; 最后, 对全文进行总结并给出未来挑战.
  • 图  1  Web of Science上的VSG论文数量与被引频次

    Fig.  1  Number and citation frequency of articles on VSG in Web of Science

    图  2  样本输入空间内虚拟与真实样本间的关系

    Fig.  2  Relationship between virtual samples and real samples in sample input space

    图  3  三维空间下的不同虚拟样本输入生成方法示意图

    Fig.  3  Diagram of different virtual sample input generation methods in 3D space

    图  4  映射模型生成虚拟样本输出流程图

    Fig.  4  Flow chart of virtual sample output generation based on mapping model

    图  5  面向分类问题的虚拟与真实样本间的关系

    Fig.  5  Relationship between virtual samples and real samples for classification problem

    图  6  面向工业过程的VSG实现流程图

    Fig.  6  Flow chart of VSG for industrial process

    图  7  VSG的研究现状结构图

    Fig.  7  Structure diagram of VSG research status

    图  8  GAN模型的结构

    Fig.  8  Structure of GAN model

    图  9  基于CGAN的VSG模型结构

    Fig.  9  VSG model structure based on CGAN

    图  10  面向VSG的原始域、可扩展域和未知域的示意图

    Fig.  10  Schematic diagram of original, extension, and unknown domain for VSG

    图  11  MTD技术

    Fig.  11  Mega-trend-diffusion

    图  12  MD-MTD示意图

    Fig.  12  Schematic figure of multi-distribution MTD

    图  13  面向回归建模问题的VSG应用统计结果

    Fig.  13  VSG application statistical results for regression modeling problem

    图  14  2019 ~ 2022年面向故障诊断领域的VSG应用统计结果

    Fig.  14  VSG application statistical results for fault diagnosis on 2019 ~ 2022

    表  1  面向分类问题的虚拟样本评价指标

    Table  1  Virtual sample evaluation index for classification problem

    评价指标文献年代
    Wasserstein距离[139]2020
    KL散度、F-score、Kappa系数、GAN测试值[66]2021
    Wasserstein距离、KL散度、欧氏距离、皮尔逊相关系数[67]2021
    马氏距离、欧氏距离[82]2021
    判别概率、最大均值差异、KL散度[69]2022
    皮尔逊相关系数[85]2022
    最大均值差异、KL散度、GAN测试值[92]2022
    下载: 导出CSV

    表  2  面向回归问题VSG的合成数据集

    Table  2  Synthetic datasets of VSG for regression problem

    基准函数取值空间文献
    $y = \left\{ {\begin{aligned} &{\sin x/x,{\rm{ if }}x \ne 0}\\ &{1,{\rm{ if }}x = 0} \end{aligned}} \right.$$x \in \left[ { - 2\pi ,2\pi } \right]$[49]
    $\begin{aligned} y = \;&2.0775 + 9.04546 \times \left( {{{10}^{ - 1}}} \right){x_1} + x_2^2 + \cos \left( {{x_3}} \right) + 1.3556 \times \left( {1.5 \times \left( {1 - {x_4}} \right)} \right){\rm{ }} +\\ &x_5^3 + {x_6} - 2.57151{x_7} - 5.09736 \times \left( {{{10}^{ - 1}}} \right) \times \left( {x_8^2} \right)\end{aligned}$$x \in \left[ {0,1} \right]$[53]
    $\begin{aligned}y = \;&0.415\sin {x_1} - 0.312x_2^2 + 1/\left( {1 + {{\rm{e}}^{ - {x_3}}}} \right) + \cos x_4^3 + 0.66{{\rm{e}}^{1 - x_5^{0.5}}}\sin {x_5}{\rm{ }} -\\ &\cos {x_6}\ln \left( {1/\cos {x_6}} \right) + 0.38\tanh {x_7} + \left( {1 - x_8^3} \right)\cos x_8^3\end{aligned}$${\rm{ }}x \in \left[ {0,1} \right]$[54]
    $\begin{aligned} &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right)\\ &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,0.01{x^2} } \right)\\ &y = \;x + 0.2\sin \left( {20x} \right) + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right) \end{aligned}$$x \in \left[ {0,1} \right]$[55]
    $\begin{aligned} y =\;& 1.3356 \times \left( {1.5\left( {1 - {x_1} } \right)} \right) + { {\rm{e} }^{2{x_1} - 1} }\sin \left( {3\pi { {\left( { {x_1} - 0.6} \right)}^2} } \right){\rm{ } } +\\ &{ {\rm{e} }^{3\left( { {x_2} - 0.5} \right)} }\sin \left( {4\pi { {\left( { {x_2} - 0.9} \right)}^2} } \right)\end{aligned}$$x \in \left[ {0,1} \right]$[57, 64, 127]
    $y = \sin \left( {{x_1}} \right) + \cos \left( {{x_2}} \right) + \sin \left( {{x_1}} \right) \times \cos \left( {{x_2}} \right)$${x_1} \in \left[ { - \pi ,\pi } \right]$[59, 129]
    ${x_2} \in \left[ {0,2\pi } \right]$
    $y = {{\rm{e}}^{(2x - 1)}}\sin \left[ {4\pi {{(x - 0.6)}^2}} \right] + \varepsilon ,{\rm{ }}\varepsilon \ \sim {\rm{N}}(0,0.0025)$$x \in \left[ {0,1} \right]$[20]
    注: $\varepsilon $是为了更好地模拟实际工业过程的环境影响而添加的噪声项
    下载: 导出CSV

    表  3  面向分类问题的VSG公开数据集

    Table  3  Public datasets of VSG for classification problem

    数据集数据集信息文献
    Case Western Reserve University由美国凯斯西储大学发布的位于轴承数据中心网站的[67, 69, 92, 132133]
    (CWRU)轴承故障数据集1轴承故障数据集, 包含无故障和滚动体、内圈和外圈故障数据.
    University of Connecticut美国康涅狄格大学Jiong Tang团队发布的齿轮箱[85]
    (UoC)齿轮箱故障故障数据集, 包括健康工况、缺齿、齿根裂纹、齿面剥落以及
    数据集2不同程度齿尖破损状态数据.
    Tennessee Eastman process由美国伊士曼化学公司开发的化学过程模拟平台生成的[156157]
    (TEP)数据集3数据集, 包括正常工况和21种异常工况数据.
    IEEE PHM 2009齿轮箱故障由2009年的IEEE PHM挑战赛提供的齿轮箱故障[80]
    数据集4数据集, 包含健康、缺齿、齿裂等8种工况.
    西安交通大学Spectra Quest (SQ)由西安交通大学SQ实验平台得到的电机轴承外圈[150]
    轴承故障数据集5和内圈故障数据集.
    数据集网址:
    1 https://engineering.case.edu/bearingdatacenter/download-data-file
    2 https://figshare.com/articles/dataset/Gear_Fault_Data/6127874/1
    3 http://depts.washington.edu/control/LARRY/TE/download.html
    4 http://www.phmsociety.org/references/datasets
    5 https://github.com/sliu7102/SQ-dataset-with-variable-speed-for-fault-diagnosis
    下载: 导出CSV

    A1  VSG的研究成果统计与对比

    A1  Statistics and comparison of VSG research results

    分类子分类方法日期优劣文献
    面向样本覆盖区域之原始域样本空间的回归VSG特征工程LLE + BPNN2020特征变换, 流形学习更加直观, 但特征失去物理含义[52]
    Isomap + 插值法2020[53]
    t-SNE + RF2021[54]
    机理2021特征选择, 工业过程知识获取困难[55]
    两者结合2020综合特征变换与选择, 具有较强的定制化特性[56]
    样本工程空间投影 + RBF2021函数模型, 空间投影具有新颖性[57]
    数据趋势2021函数模型, 提出的稀疏假设和集中假设具有参考价值[49]
    总线拓扑结构插值2023函数模型, 有效控制插值位置[58]
    RWNN插值法2018函数模型, 基于神经网络[59]
    AANN插值2019模型学习样本的非线性分布关系,[60]
    RWNN + 等间隔插值法2020对小样本难以有效[56]
    MTD + PSO2021函数模型, PSO优化选择虚拟样本[61]
    多目标PSO2022函数模型, 多目标PSO优化选择虚拟样本和生成数量[15]
    LOF + K-means + GAN2021对抗模型, 插值生成输出, CGAN生成输入[20]
    双GAN2022对抗模型, 两种GAN分别负责输入和
    输出的生成, 复杂性高
    [64]
    回归器 + CWGAN2022对抗模型, 通过回归器匹配虚拟样本输出并共同训练[65]
    面向样本区域之原始域样本空间的分类VSG特征工程添加编码器2020采用编码器提取特征[68]
    添加卷积层2021添加卷积层提取特征[66]
    添加卷积层2021[67]
    添加自注意2022添加自注意力模型增强特征[69]
    面向样本区域之原始域样本空间的分类VSG样本工程基于加权核的SMOTE2018函数模型, 解决SMOTE算法在高IR下
    的非线性可分离问题
    [71]
    Minkowski距离替换欧氏距离2019函数模型, 有效生成高维虚拟样本[72]
    SMOTE + 决策树2020函数模型, 决策树算法提取关键规则[73]
    SMOTE + SVM2020函数模型, 支持向量边界生成虚拟样本[74]
    范围控制SMOTE2021函数模型, 有效地缓解范围偏移和边界样本重叠等问题[75]
    超球面空间 + 组发现2007函数模型, 由数据的结构生成虚拟样本[76]
    组发现技术 + 纯化过程2020函数模型, 纯化过程剔除冗余样本[77]
    ACGAN2020对抗模型, 添加dropout层防止过拟合, 添加卷积层提取更多特征[79]
    ACWGAN-GP2020对抗模型, ACGAN的进化版[80]
    MAML + ACGAN2021对抗模型, MAML初始化和更新网络使得生成过程更加稳定[81]
    GAN + 多尺度CNN2021对抗模型, 生成模型需要改进[82]
    DCGAN + K-means2021对抗模型, K-means算法对模型改进[67]
    MoGAN2021对抗模型, 判别器既判断样本真假又充
    当分类器和故障检测器
    [83]
    GAN + MSCNN2021对抗模型, 多GAN联合生成[82]
    贝叶斯优化 + WGAN2021对抗模型, 贝叶斯优化策略自适应调节判别器参数[86]
    WGAN + LSTM-FCN2022对抗模型, 结合LSTM[84]
    AE + GAN2019对抗模型, AE结合GAN[89]
    VAE + GAN2020[68]
    深度残差网络 + VAE + GAN2021对抗模型, 深度残差网络提高模型性能[90]
    AE + LSGAN2022对抗模型, 暹罗编码器计算特征残差[91]
    CVAEGAN-SM2022对抗模型, 生成器中加入自调制机制[92]
    堆叠AE + WGAN2023对抗模型, 提升了模型的生成能力[93]
    面向样本覆盖区域之扩展域样本空间的的回归VSG集合理论正态隶属度1997模糊集理论, 仅适用于扩展范围对称情况[94]
    DNN2003模糊集理论, 特征相关系数大于0.9才能计算扩展范围[95]
    MTD2007模糊集理论, 通过假设特征独立不对称地扩散特征范围[96]
    GTD2010模糊集理论, 增量版的MTD[97]
    TTD2012模糊集理论, 与树算法结合[98]
    神经网络MTD2012模糊集理论, 神经网络与MTD结合[99]
    MD-MTD2016模糊集理论, 三角和均匀分布组合的多分布[100]
    KNN + MTD2022模糊集理论, KNN确保合理的扩展范围[101]
    K-means + MTD2022模糊集理论, K-means解决属性冗余[102]
    AD-MTD + MD-MTD2019模糊集理论, 多种算法结合取长补短[103]
    MTD + RWNN2020模糊集理论, 改进分布 + 隐含层插值[56]
    MTD + GA2014模糊集理论, 基于优化算法搜寻虚拟样本, 更合理[46]
    TMIE + PSO2016模糊集理论, PSO优化选择虚拟样本[104]
    MTD + PSO2021[61]
    分布假设IKDE2006高斯分布, 改进KDE分布[109]
    时序IKDE2008高斯分布, 用于时序数据[110]
    SJDT2016高斯分布, SJDT将数据趋于正态分布[111]
    MPV2013非高斯分布, 多样本分布[112]
    假设检验2019非高斯分布, 先聚类再估计[113]
    基于知识多目标PSO2022基于知识确定输出扩展域下限, 多目标PSO优化选择虚拟样本和生成数量[15]
    面向样本覆盖区域之扩展域样本空间的分类VSG集合理论FID2017模糊集理论, 既生成虚拟样本又填充缺失[114]
    SMOTE + 粗糙集理论2012粗糙集理论, 扩展范围有限[115]
    三支决策2018粗糙集理论, 未精准计算扩展范围[116]
    分布假设假设分布2010高斯分布, 计算数据的均值和方差确定高斯分布[43]
    假设分布2022高斯分布, AIC和BIC自适应确定高斯分布参数[117]
    SVM2013非高斯分布, 状态函数采样生成虚拟样本[118]
    K-means + Weibull分布2014非高斯分布, 特定过程采用特定分布[119]
    基于知识FAGAN2021基于知识, 专家知识定义的故障属性作为辅助信息以使得生成样本[123]
    面向VSG实现流程之回归问题过程数据预处理阶段缺失值删减和人工填充2021有效减少缺失和异常值对数据的影响但会减少样本数量[61]
    2022[15]
    缺失和异常值识别剔除2020[127]
    2022[64]
    LLE2020流形学习更加直观, 特征失去物理含义[52]
    Isomap2020[53]
    t-SNE2021[54]
    根据化工机理选择特征2017机理知识获取困难[128]
    2018[59]
    专家经验2021特定实验知识[61]
    虚拟样本输入生成阶段欧氏距离识别稀疏区域2020引入欧氏距离[127]
    投影最大间距识别稀疏2021引入投影最大间距[57]
    可视化样本分布识别稀疏区域2020可视化, 直观[52]
    2020[53]
    2021[54]
    稀疏性和集中性假设2021确定稀疏和密集区域关系[49]
    WGAN-GP2022引入GAN用于回归[64]
    CWGAN2022[65]
    MTD2007确定虚拟样本输入的扩展域范围后插值[96]
    TMIE + PSO2016[104]
    流形子空间 + MTD2021[129]
    虚拟样本输出生成阶段RWNN映射模型2018映射模型的性能受限于小样本[59]
    2016[104]
    2021[129]
    BPNN映射模型2020[52]
    RF映射模型2021[54]
    RBF映射模型2021[57]
    CS-CGAN匹配输出2022匹配模型与虚拟样本输入同时训练[64]
    回归器匹配输出2022[65]
    分位数回归器匹配输出2021[55]
    虚拟样本质量筛选阶段模型误差小于10%筛选2014受限于小样本建模性能[46]
    隶属度的似然估计筛选2018引入似然估计[130]
    PSO优化算法筛选2021引入优化算法[61]
    专家筛选2021具有主观性[49]
    虚拟样本数量确定阶段信息熵2019引入信息熵[131]
    稀疏和集中假设2021引入各种假设[49]
    特殊阶段LOF + CGAN2021先生成虚拟样本输出后匹配虚拟样本输入[20]
    三样条插值 + ITNN2021[49]
    面向VSG实现流程之分类问题过程数据预处理阶段信号数据转换为灰度图2022借鉴图像领域算法处理[85]
    2022[132]
    虚拟样本输入生成阶段重叠分割、旋转和抖动的数据增强2021缓解过拟合[133]
    SMOTE + 粗糙集理论2012扩展范围有限[115]
    SMOTE + 决策树2020决策树算法提取运行规则[73]
    SMOTE + SVM2020引入支持向量机边界[74]
    Minkowski距离替换欧氏距离2019可以有效地生成高维虚拟样本[72]
    范围控制SMOTE2021通过控制生成范围减少边界重叠样本[75]
    WGAN + LSTM-FCN2022引入LSTM[84]
    CWGAN-GP + FDGRU2022增加梯度惩罚项和条件信息[85]
    Pull-away损失函数GAN2022添加自注意力模型增强特征[69]
    ACGAN2020添加dropout层防止过拟合, 添加卷积层提取更多特征[79]
    ACGAN + CVAE2020引入CVAE[68]
    深度残差网络 + VAE + GAN2021深度残差网络提高模型性能[90]
    MoGAN2021判别器包含真假判断、故障诊断和故障分类三种功能[83]
    CVAEGAN-SM2022生成器加入自调制机制[92]
    并行GAN2021对应多类别同时训练, 复杂性高[19]
    SMOTE + VAE2018基于样本的迁移学习VSG[134]
    自适应混合2020[135]
    迁移学习 + 插值2022[136]
    Fine-tuning + WGAN2021基于模型的迁移学习VSG[137]
    迁移学习 + GAN2022[138]
    虚拟样本质量筛选阶段Wasserstein距离2020未给出评价指标的具体限值筛选虚拟样本[139]
    KL散度, F-score,2021[66]
    Kappa系数, GAN测试值
    Wasserstein距离, KL散度, 欧氏2021[67]
    距离, 皮尔逊相关系数
    马氏距离, 欧氏距离2021给出评价指标的具体限值筛选虚拟样本[82]
    判别概率, 最大均值差异, KL散度2022[69]
    皮尔逊相关系数2022未给出评价指标的具体限值筛选虚拟样本[85]
    最大均值差异, KL散度,2022[92]
    GAN测试值
    虚拟样本数量确定阶段分类复杂度确定虚拟样本数量2021采用分类复杂度确定虚拟样本数量[14]
    面向VSG推广应用的回归问题石油化工TMIE + PSO2016PSO优化选择[104]
    RWNN插值法2018提出隐含层插值生成虚拟样本[59]
    Isomap + 插值法2020[53]
    分位数回归器匹配输出2021提出分位数回归匹配输出[55]
    回归器 + CWGAN2022通过回归器匹配虚拟样本输出并同时训练[65]
    固废焚烧两者结合2020具有较强的定制化特性[56]
    MTD + PSO2021PSO优化选择虚拟样本[61]
    多目标PSO2022多目标PSO优化选择虚拟样本和生成数量[15]
    工业制造GTD2010增量版的MTD[97]
    TTD2012与树算法结合[98]
    MPV2013采用多分布[112]
    模糊c均值聚类 + 箱线图2018箱线图确定扩展范围[141]
    假设分布2022AIC和BIC自适应确定高斯分布参数[117]
    矿业冶金时频变换 + FBP + 信息熵2018特定问题采用特定方法[6]
    RWNN插值 + MD-MTD2022GA优化选择虚拟混合样本[145]
    面向VSG推广应用的分类问题滚动轴承
    故障诊断
    迁移学习 + GAN2020迁移与GAN相结合[146]
    PGDAE + DCN2021引入PGDAE[147]
    元学习 + WAE2021元学习提高虚拟样本质量[66]
    CVAEGAN-SM2022生成器加入自调制机制[92]
    DSAN2022自注意模块增强深度特征[132]
    GAN2022常数Q转换将信号转换为频谱图, 均方差替换交叉熵[148]
    ACGAN2022引入ACGAN[149]
    特征增强GAN2022自注意模块增强深度特征[69]
    DFGN2021可用于零样本故障诊断[150]
    变压器故障诊断SMOTE + 决策树2020决策树算法提取关键规则[73]
    SMOTE + SVM2020提出支持向量边界生成样本[74]
    CWGAN-GP2020引入梯度惩罚[151]
    AE + LSGAN2022暹罗编码器计算特征残差[91]
    涡轮机故障诊断GAN2019结合GAN与具体问题[152]
    DACNN2019[153]
    VAE + GAN2019[89]
    1D-CNN GAN2019虚拟样本输出和故障诊断组合模型[154]
    齿轮箱故障诊断ACGAN + CVAE2020引入CVAE[68]
    贝叶斯优化 + WGAN2021贝叶斯优化策略自适应调节判别器参数[86]
    DCGAN + K-means2021K-means算法对模型改进[67]
    下载: 导出CSV

    A2  符号说明

    A2  Symbol description

    缩写词英文全称中文全称
    VSGVirtual sample generation虚拟样本生成
    MSWIMunicipal solid waste incineration城市固废焚烧
    DXNDioxin二噁英
    VAEVariational autoencoder变分自编码器
    GANGenerative adversarial network生成对抗网络
    FDDFault detection and diagnosis故障检测与诊断
    IRImbalance ratio不平衡比
    SMOTESynthetic minority over-sampling technique合成少数类过采样技术
    MAPEMean absolute percentage error平均绝对百分比误差
    LLELocally linear embedding局部线性嵌入
    BPNNBack propagation neural network反向传播神经网络
    IsomapIsometric feature mapping等距特征映射
    t-SNEt-distributed stochastic neighbor embeddingt分布随机邻域嵌入
    RFRandom forest随机森林
    RBFRadial basis function径向基函数
    CSICubic spline interpolation三样条插值
    ITNNInput-training neural network输入训练神经网络
    RWNNRandom weight neural network随机权神经网络
    AANNAuto-associative neural network自联想神经网络
    LOFLocal outlier factor局部异常因子
    CGANConditional generative adversarial network条件生成对抗网络
    CS-CGANCycle structure conditional generative adversarial network循环结构条件生成对抗网络
    FFTFast Fourier transform快速傅里叶变换
    SVMSupport vector machine支持向量机
    AC-GANAuxiliary classifier generative adversarial network辅助分类器生成对抗网络
    ACWGAN-GPAuxiliary classier Wasserstein generative adversarial network with gradient具有梯度惩罚的辅助分类Wasserstein生成对抗网络
    penalty
    MAMLModel agnostic meta learning模型无关元学习
    CNNConvolutional neural network卷积神经网络
    DCGANDeep convolutional generative adversarial network深度卷积生成对抗网络
    MoGANMinority oversampling generative adversarial network少数类过采样生成对抗网络
    AEAutoencoder自编码器
    CVAE-GANConditional variational autoencoder generative adversarial network条件变分自编码器生成对抗网络
    LSGANLeast squares generative adversarial network最小二乘生成对抗网络
    DNNDiffusion neural network扩散神经网络
    MTDMega-trend-diffusion大趋势扩散
    GTDGeneralized-trend-diffusion广义趋势扩散
    TTDTree structure based trend diffusion树结构趋势扩散
    MD-MTDMulti-distribution mega-trend-diffusion多分布大趋势扩散
    KNNK-nearest neighborK近邻
    AD-MTDAdvanced mega-trend-diffusion改进型大趋势扩散
    Hybrid-MTDHybrid mega-trend-diffusion混合大趋势扩散
    GAGenetic algorithm遗传算法
    FBPFeasibility-based programming可行性的规划
    TMIEInformation-expanded based on triangular membership基于三角隶属度的信息扩散
    PSOParticle swarm optimization粒子群优化
    IKDEImproved kernel density estimation改善核密度估计
    SJDTSmall Johnson data transformation小型约翰变换方法
    MPVMaximal p value最大p
    AICAkaike information criterion赤池信息准则
    AICcCorrected version of the akaike information criterion修正版赤池信息准则
    FIDFuzzy-based information decomposition基于模糊的信息分解
    BICBayesian information criterion贝叶斯信息准则
    FAGANFault attributes generative adversarial network故障属性生成对抗网络
    SRWGANSemantic refinement Wasserstein generative adversarial network 语义细化Wasserstein生成对抗网络
    MOPSOMulti-objective particle swarm optimization多目标粒子群优化
    PGDAEPredictive generative denoising autoencoder预测生成去噪自编码器
    DCNDeep coral network深度珊瑚网络
    WAEWasserstein autoencoderWasserstein自编码器
    DSANDeep subdomain adaptation network深度子域适应网络
    DFGNDeep feature generating network深度特征生成网络
    DACNNDeep adversarial convolutional neural network深度对抗卷积神经网络
    BOBayesian optimization贝叶斯优化
    DCGANDeep convolution generative adversarial network深度卷积生成对抗网络
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-30
  • 录用日期:  2023-05-18
  • 网络出版日期:  2023-08-14

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