基于粒子群优化广义回归神经网络的船用柴油机废气温度基线模型

IF 1.7 4区工程技术 Q2 ENGINEERING, MARINE Ships and Offshore Structures Pub Date : 2024-04-10 DOI:10.1080/17445302.2024.2338680

Hong Zeng, Jianping Sun, Cai Chen, Kuo Jiang, Zefan Wu

引用次数: 0

摘要

本研究针对传统船用柴油机（MDE）可靠性和稳定性状态监测的局限性，提出了一种混合机器学习和深度学习（DL）模型计算方法。

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来源期刊

Ships and Offshore Structures ENGINEERING, MARINE-

CiteScore

4.50

自引率

14.30%

发文量

164

审稿时长

7.5 months

期刊介绍： Ships and Offshore Structures is an international, peer-reviewed journal which provides an authoritative forum for publication and discussion of recent advances and future trends in all aspects of technology across the maritime industry. The Journal covers the entire range of issues and technologies related to both ships (including merchant ships, war ships, polar ships etc.) and offshore structures (floating and fixed offshore platforms, offshore infrastructures, underwater vehicles etc.) with a strong emphasis on practical design, construction and operation. Papers of interest to Ships and Offshore Structures will thus be broad-ranging, and will include contributions concerned with principles, theoretical/numerical modelling, model/prototype testing, applications, case studies and operational records, which may take advantage of computer-aided methodologies, and information and digital technologies. Whilst existing journals deal with technologies as related to specific topics, Ships and Offshore Structures provides a systematic approach to individual technologies, to more efficiently and accurately characterize the functioning of entire systems. The Journal is intended to bridge the gap between theoretical developments and practical applications for the benefit of academic researchers and practising engineers, as well as those working in related governmental, public policy and regulatory bodies.