Convolutional Neural Network Algorithm and Case

Chen Xu; Zhang Jun; Chen Wen-wei; Li Shuo-hao

doi:10.12052/gdutxb.170093

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Convolutional Neural Network Algorithm and Case

更新时间：2026-05-27

- Convolutional Neural Network Algorithm and Case
- Vol. 34, Issue 6, Pages: 20-26(2017)
- 作者机构：
  
  国防科技大学信息系统与管理学院,湖南,长沙,410073
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170093
  CLC： TP391
- Online First：09 November 2017，
  
  Published：2017
- 稿件说明：
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陈旭, 张军, 陈文伟, 李硕豪. 卷积网络深度学习算法与实例[J]. 广东工业大学学报, 2017, 34(6): 20-26.

Chen Xu, Zhang Jun, Chen Wen-wei, et al. Convolutional Neural Network Algorithm and Case[J]. 2017, 34(6): 20-26.
陈旭, 张军, 陈文伟, 李硕豪. 卷积网络深度学习算法与实例[J]. 广东工业大学学报, 2017, 34(6): 20-26. DOI： 10.12052/gdutxb.170093.

Chen Xu, Zhang Jun, Chen Wen-wei, et al. Convolutional Neural Network Algorithm and Case[J]. 2017, 34(6): 20-26. DOI： 10.12052/gdutxb.170093.

摘要

卷积神经网络(CNN)和反向传播神经网络结合组成的深度学习模型

是目前应用比较广泛的深度学习模型

但是具体算法有多种. 本文针对以CNN为主体的深度学习算法的实现技术

包括卷积核设计、池化的作用、激活函数的选取和训练过程等问题进行详细讨论

并结合实例进行说明

便于对卷积网络深度学习算法的掌握

使其能得到更广泛的应用. 最后总结并展望卷积神经网络未来的研究方向.

Abstract

Convolutional neural network (CNN) is a deep learning model with strong expression and classification ability

and is currently widely used

but there are a variety of specific algorithms. In this paper

the realization of deep learning algorithm based on convolutional neural network CNN

including the function of convolution kernel

the role of pooling

the selection of activation function and the training process are discussed. And one example is explained

which facilitates the mastery of CNN. Finally

the future research direction of convolution neural network is summarized and forecasted.

关键词

Keywords

references

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