Ship spare parts demand forecast based on RBF neural network

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.