An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine-Reference-Cited by-同舟云学术

An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine

Published:2023-10 Issue: Volume:141 Page:108513
ISSN:1270-9638
Container-title:Aerospace Science and Technology
language:en
Short-container-title:Aerospace Science and Technology

Author:

Xu Maojun,Wang Kang,Li Ming^ORCID,Geng Jia,Wu Yun,Liu Jinxin,Song Zhiping

Funder

National Natural Science Foundation of China

National Major Science and Technology Projects of China

Publisher

Elsevier BV

Subject

Aerospace Engineering

Reference45 articles.

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4. Life cycle gas path performance monitoring with control loop parameters uncertainty for aeroengine;Jin;Aerosp. Sci. Technol.,2021

5. Uncertainties in gas-path diagnosis of gas turbines: representation and impact analysis;Liao;Aerosp. Sci. Technol.,2021

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