电子科技大学学报2026，Vol.28Issue(4)：580-589,10.DOI:10.12178/1001-0548.2025030
基于小样本元学习算法的弹药装备质量预测
Small sample data-based meta-learning algorithm for equipment quality prediction
摘要
Abstract
To solve the difficulty of munition equipment quality prediction under small sample data,a munition equipment quality prediction model based on meta-learning algorithm is proposed in this paper.Firstly,the pre-processed ammunition equipment quality data is divided into multiple batches of task sequences to meet the input requirements of the small sample data meta-learning model.Secondly,the munition equipment quality meta-learning model is trained by a series of learning tasks to acquire the knowledge for predicting munition equipment quality,and when dealing with the tasks that have never been encountered before,the knowledge learned can help improve adaptability and generalization in learning these new tasks.Finally,the quality prediction validity of munition equipment quality meta-learning model is verified by open data set and munition equipment quality data set.关键词
弹药装备质量预测/小样本数据/多批次任务序列/元学习Key words
ammunition equipment quality/small sample data/multiple batches of task sequences/meta-learning分类
信息技术与安全科学引用本文复制引用
石义官,金鑫,曹亚召,陈勇,张龙杰..基于小样本元学习算法的弹药装备质量预测[J].电子科技大学学报,2026,28(4):580-589,10.基金项目
国家社科基金一般项目(23BKS122) （23BKS122）
2025年度江苏省研究生科研创新项目(KYCX25_0669). （KYCX25_0669）
