自编码器及其应用综述

来杰; 王晓丹; 向前; 宋亚飞; 权文

doi:10.11959/j.issn.1000-436x.2021160

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自编码器及其应用综述

综述 | 更新时间：2024-06-05

- 自编码器及其应用综述
- Review on autoencoder and its application
- 通信学报 2021年42卷第9期页码：218-230
- 作者机构：
  
  1. 空军工程大学防空反导学院，陕西西安 710051
  2. 空军工程大学空管领航学院，陕西西安 710051
- 作者简介：
  
  [ "来杰（1994− ），男，四川简阳人，空军工程大学博士生，主要研究方向为机器学习及其在目标识别、入侵检测等领域中的应用" ]
  [ "王晓丹（1966− ），女，陕西汉中人，博士，空军工程大学教授，主要研究方向为机器学习及其在目标识别、入侵检测等领域中的应用" ]
  [ "向前（1995− ），男，四川广元人，空军工程大学博士生，主要研究方向为机器学习及其在目标识别、入侵检测等领域中的应用" ]
  [ "宋亚飞（1988− ），男，河南汝州人，博士，空军工程大学副教授，主要研究方向为机器学习及其在目标识别、入侵检测等领域中的应用" ]
  [ "权文（1988− ），女，陕西蒲城人，博士，空军工程大学讲师，主要研究方向为机器学习及其在空管领航、目标识别等领域中的应用" ]
- 基金信息：
  
  国家自然科学基金资助项目(61876189);国家自然科学基金资助项目(61806219);国家自然科学基金资助项目(61703426);陕西省自然科学基础研究计划基金资助项目(2021JM—226)
- DOI：10.11959/j.issn.1000-436x.2021160
  中图分类号： TP183
- 网络首发：2021-09，
  
  纸质出版：2021-09-25
- 稿件说明：
移动端阅览
来杰, 王晓丹, 向前, 等. 自编码器及其应用综述[J]. 通信学报, 2021,42(9):218-230.

Jie LAI, Xiaodan WANG, Qian XIANG, et al. Review on autoencoder and its application[J]. Journal on Communications, 2021, 42(9): 218-230.
来杰, 王晓丹, 向前, 等. 自编码器及其应用综述[J]. 通信学报, 2021,42(9):218-230. DOI： 10.11959/j.issn.1000-436x.2021160.

Jie LAI, Xiaodan WANG, Qian XIANG, et al. Review on autoencoder and its application[J]. Journal on Communications, 2021, 42(9): 218-230. DOI： 10.11959/j.issn.1000-436x.2021160.

摘要

自编码器作为典型的深度无监督学习模型，能够从无标签样本中自动学习样本的有效抽象特征。近年来，自编码器受到广泛关注，已应用于目标识别、入侵检测、故障诊断等众多领域中。基于此，对自编码器的理论基础、改进技术、应用领域与研究方向进行了较全面的阐述与总结。首先，介绍了传统自编码器的网络结构与理论推导，分析了自编码器的算法流程，并与其他无监督学习算法进行了比较。然后，讨论了常用的自编码器改进算法，分析了其出发点、改进方式与优缺点。接着，介绍了自编码器在目标识别、入侵检测等具体领域的实际应用现状。最后，总结了现有自编码器及其改进算法存在的问题，并展望了自编码器的研究方向。

Abstract

As a typical deep unsupervised learning model

autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years

autoencoder has been widely used in target recognition

intrusion detection

fault diagnosis and many other fields.Thus

the theoretical basis

improved methods

application fields and research directions of autoencoder were described and summarized comprehensively.At first

the network structure

theoretical derivation and algorithm flow of traditional autoencoder were introduced and analyzed

and the difference between autoencoder and other unsupervised learning algorithms was compared.Then

common improved autoencoders were discussed

and their innovation

improvement methods and relative merits were analyzed.Next

the practical application status of autoencoder in target recognition

intrusion detection and other fields were introduced.At last

the existing problems of autoencoder were summarized

and the possible research directions were prospected.

关键词

Keywords

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