Physics-Informed Neural Networks With Weighted Losses by Uncertainty Evaluation for Accurate and Stable Prediction of Manufacturing Systems

理论(学习稳定性) 钥匙(锁) 差异(会计) 人工神经网络 计算机科学 复杂系统 数据挖掘 机器学习 预测建模 人工智能 计算机安全 会计 业务
作者
Jiaqi Hua,Yingguang Li,Changqing Liu,Peng Wan,Xu Liu
出处
期刊:IEEE transactions on neural networks and learning systems [Institute of Electrical and Electronics Engineers]
卷期号:35 (8): 11064-11076 被引量:22
标识
DOI:10.1109/tnnls.2023.3247163
摘要

The state prediction of key components in manufacturing systems tends to be risk-sensitive tasks, where prediction accuracy and stability are the two key indicators. The physics-informed neural networks (PINNs), which integrate the advantages of both data-driven models and physics models, are deemed as an effective approach and research trends for stable prediction; however, the potential advantages of PINN are limited for the situations with inaccurate physics models or noisy data, where the balancing of the weights of the data-driven model and physics model is very important for improving the performance of PINN, and it is also a challenge urgently to be addressed. This article proposed a kind of PINN with weighted losses (PNNN-WLs) by uncertainty evaluation for accurate and stable prediction of manufacturing systems, where a novel weight allocation strategy based on uncertainty evaluation by quantifying the variance of prediction errors is proposed, and an improved PINN framework is established for accurate and stable prediction. The proposed approach is verified with open datasets on tool wear prediction, and experimental results show that the prediction accuracy and stability could be obviously improved over existing methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小二郎应助dhyzf1214采纳,获得30
刚刚
1秒前
2秒前
scvrl完成签到,获得积分10
3秒前
3秒前
3秒前
这个柳絮不会飞完成签到,获得积分10
3秒前
LCR完成签到,获得积分10
4秒前
猪猪hero发布了新的文献求助30
4秒前
aiyoualxb发布了新的文献求助10
4秒前
4秒前
5秒前
李芬完成签到,获得积分10
5秒前
d叨叨鱼发布了新的文献求助20
6秒前
7秒前
hino发布了新的文献求助10
7秒前
mizhou完成签到,获得积分20
7秒前
9秒前
猪猪hero发布了新的文献求助10
9秒前
河马发布了新的文献求助10
10秒前
11秒前
Peggy完成签到 ,获得积分10
12秒前
12秒前
张雷应助景穆采纳,获得20
12秒前
CANDYY完成签到,获得积分10
13秒前
小西完成签到,获得积分10
13秒前
14秒前
Liufgui应助七七采纳,获得10
14秒前
崔佳鑫发布了新的文献求助10
15秒前
15秒前
香蕉觅云应助杜兰特工队采纳,获得10
15秒前
XHQ发布了新的文献求助10
15秒前
aiyoualxb完成签到,获得积分10
16秒前
WantoXi发布了新的文献求助10
16秒前
18秒前
18秒前
strzeng发布了新的文献求助10
18秒前
19秒前
ffff完成签到,获得积分20
19秒前
虾米发布了新的文献求助10
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988827
求助须知:如何正确求助?哪些是违规求助? 3531183
关于积分的说明 11252671
捐赠科研通 3269809
什么是DOI,文献DOI怎么找? 1804780
邀请新用户注册赠送积分活动 881885
科研通“疑难数据库(出版商)”最低求助积分说明 809021