基于深度强化学习的自动特征提取模型研究  

Research on Automatic Feature Extraction Model Based on Deep Reinforcement Learning

作　　者：程凤敏[1] CHEN Fengmin(Weifang University of Science and Technology,Weifang 262700,China)

机构地区：[1]潍坊科技学院,山东潍坊262700

出　　处：《电子质量》2024年第4期11-15,共5页Electronics Quality

摘　　要：介绍了深度强化学习在自动特征提取领域的应用。首先,简单地概述了现有的特征提供取方法,包括传统的特征取方法、基于深度学习的特征提取方法和基于深度强化学习的特征提取方法;其次,阐述了深度强化学习和图像处理技术;最后,详细地介绍了基于深度强化学习的自动特征提取模型的设计方法,包括数据集准备与预处理、模型架构设计、模型训练过程及参数设置、实验结果分析与评估。该模型能够提高图像处理的准确性和效率,具有较高的推广使用价值。The application of deep reinforcement learning in automatic feature extraction is introduced.Firstly,the existing feature extraction methods are briefly summarized,including traditional feature extraction methods,deep learning-based feature extraction methods and deep reinforcement learning-based feature extraction methods.Then,the deep reinforcement learning and image processing technology are described.Finally,the design method of automatic feature extraction model based on deep reinforcement learning is introduced in detail,including data set preparation and processing,model architecture design,model training process and parameter setting,analysis and evaluation of experimental results.This model can improve the accuracy and efficiency of image processing,and has a high popularization value.

关 键 词：深度强化学习 自动特征提取 图像处理 模型训练 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]