基于BP神经网络的液压缸市场价格估算模型

Hydraulic cylinders are widely used with various kinds and a large variation range of price. It has great significance to establish hydraulic cylinder market price forecast model for the issues of making procurement plan and economical efficiency evaluation of maintenance and remanufacturing. However, there are many influencing factors of hydraulic cylinder market price and it is hard to establish a direct mathematical model. The objective of this paper is to develop a forecast model of market price for the hydraulic cylinder via the application of back-propagation (BP) neural networks. Seven critical factors related to market price of hydraulic cylinder have been found out by the means of Delphi, which are cylinder bore diameter, stroke, pressure rating, servo cylinder or not, installation type, manufacturer qualification and Producer Price Index (PPI). Case study is used to prove that neural networks are capable of forecasting market price of hydraulic cylinder. The index PPI introduced in the model can improve the model sensitivity of market change to make the forecast more accurately.?????