Multiparameter Sensitivity Analysis of Operational Energy Efficiency for Inland River Ships Based on Backpropagation Neural Network Method

被引:36
作者
Yan, Xinping
Sun, Xing
Yin, Qizhi
机构
[1] Wuhan Univ Technol, Reliabil Engn Inst, Wuhan 430063, Peoples R China
[2] Wuhan Univ Technol, Key Lab Marine Power Engn & Technol, Wuhan 430063, Peoples R China
基金
中国国家自然科学基金;
关键词
energy efficiency; artificial neural network; sensitivity;
D O I
10.4031/MTSJ.49.1.5
中图分类号
P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
With the introduction of,energy effiCiency operational indicator (g01):to inland river ships, a Oultipararrieter sensitivity analysis rilethod: was proposed tO3analyZe the parameters affecting the operational energy efficiency of inland river Ships On the Oasis of experimental data, a,mod0 based on a,paCkpropagation 417t if ielat neural fletwbrki(BP-ANN) 1,10- predicting the EEGI,wat set Ca. The accuracy of this predictive' model was verified On the basis of weightSland threshOld, values of eachNariable parameter gained in the trained BP-ANN, a Gerson algorithm was used for calculating the parameter sensitivity faCtorfiesults showed that besides the engine speed, the " ehVironifient cenditiofis would alsb play a big part in the operational energy efficiency ofitilanaririvWships:The conclusion providesa foundation for engaging the energy efflp'encimprovemerit strategieSfor inland river Ships
引用
收藏
页码:148 / 153
页数:6
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