Ship spare parts demand forecast based on RBF neural network

被引：0

作者：

盖强 ^{[1
]}

刘勇 ^{[1
]}

赵宏宇 ^{[2
]}

机构：

[1] Department of Naval Gun,Dalian Naval Academy

[2] Department of International Military Exchange,Dalian Naval Academy

来源：

Journal of Measurement Science and Instrumentation | 2013年 / 4卷 / 02期

关键词：

spare parts forecast; neural network; e q uipment support;

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算]; U672 [船舶保养、修理和拆船工艺];

学科分类号：

081104 ; 0812 ; 082402 ; 0835 ; 1405 ;

摘要：

Due to the fact that in ship maintena n ce process,the method of determining the number of spare parts is not scientific and the actual operation is complicated,this paper analyzes four major facto rs affecting the number of ship spare parts,including number of main planned op eration s,total times of disassembling in maintenance,accumulated working time and mea n t ime between failures.It also establishes a spare parts demand forecast model b ased on the affecting factors and radial-basis function(RBF) neural network.F inally,the paper provide s forecast examples and makes a comparison between the examples and back propaga tion(BP) neura l network forecast result.The comparison results s how that the forecast based on RBF neural network is simple and the forecast res ult fits the actual situa tion and fitting effect is better than that based on BP.

引用

页码：167 / 169

页数：3

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