基于蚁群优化和模糊Petri网的建筑工程成本预测研究（英文）  

Research on cost prediction of construction projects based on ant colony optimization and fuzzy Petri net

作　　者：黄良辉[1] 王淑苹 王从祥[3] Liang-hui HUANG;Shu-ping WANG;Cong-xiang WANG(Architectural Engineering Institute,Tianhe College of Gnangdong Polytechnical Normal University,Guangzhou 510540,China;School of Architecture and Art,Guangdong Nanhua Vocational College of Industry and Commerce,Guangzhou 510507,China;School of Materials Science and Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]广东技术师范学院天河学院建筑工程学院,广州510504 [2]广东南华工商职业学院建艺学院,广州510507 [3]武汉理工大学材料科学与工程学院,武汉430070

出　　处：《机床与液压》2018年第24期68-73,共6页Machine Tool & Hydraulics

基　　金：Guangdong Education Department,2017 Guangdong Key Discipline-Management Science and Engineering,GJYH(2017)No.1~~

摘　　要：为了有效提高建筑工程成本动态控制的精确度,提出将蚁群优化算法和模糊Petri网理论应用于建筑工程成本预测。首先,通过模糊产生式规则选择样本工程并确定工程之间的相似度,以便建立工程成本预测模型,其权值和阈值等参数由BP神经网络训练得出。然后,利用蚁群优化对模型各参数进行优化,从而进一步提高工程成本预测的精确度。实际建筑工程实例分析结果表明:相比传统的BP神经网络预测方法,提出的方法具有更高的准确度,能够有效应用于企业建筑工程成本的科学管理。In order to effectively improve the accuracy of dynamic control of construction cost,the ant colony optimization algorithm and fuzzy Petri net theory are adopted to predict the construction cost.First,the sample project is selected by the rules of fuzzy production and the similarity between the projects is determined so as to establish the project cost forecasting model.The weights and thresholds are trained by BP neural network.Then,the parameters of the model are optimized by using ant colony optimization,so as to further improve the accuracy of project cost forecasting.The results of actual construction project show that compared with the traditional BP neural netw.ork prediction method,the proposed method has higher accuracy and can be effectively applied to the scientific management of construction cost.

关 键 词：建筑工程 工程成本预测 模糊PETRI网 蚁群优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]