您的位置:首页  > 论文页面

基于BP神经网络的液压缸市场价格估算模型

发表时间:2014-05-31  浏览量:1657  下载量:724
全部作者: 周敏,高挺,陈艳霞
作者单位: 武汉科技大学机械自动化学院
摘 要: 针对液压缸价格估算的影响因素多、难以建立直接的数学模型问题,采用德尔菲法,通过总结本领域内多位专家的经验知识,找出了内径、行程、压力等级、是否伺服缸、安装形式、厂家资质和生产者物价指数(producer price index,PPI)等7个影响液压缸价格的关键因素,建立了基于BP神经网络的液压缸市场价格估算模型,并用实例验证了该模型的有效性。模型中所引入的PPI可以提高该模型对市场环境变化的敏感度,使基于该模型的市场价格估算准确度更高。
关 键 词: 决策分析;工业工程;液压缸;BP神经网络;市场价格;估算模型
Title: A market price forecast model of hydraulic cylinders based on BP neural network
Author: ZHOU Min, GAO Ting, CHEN Yanxia
Organization: College of Machinery and Automation, Wuhan University of Science and Technology
Abstract: 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 BP neural network. 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 network is capable of forecasting market price of hydraulic cylinder. The PPI introduced in the model can improve the model sensitivity of market change to make the forecast more accurately.
Key words: decision analysis; industrial engineering; hydraulic cylinder; BP neural network; market price; forecast model
发表期数: 2014年5月第10期
引用格式: 周敏,高挺,陈艳霞. 基于BP神经网络的液压缸市场价格估算模型[J]. 中国科技论文在线精品论文,2014,7(10):1010-1014.
 
0 评论数 0
暂无评论
友情链接