[1]刘婧,叶青.采用BP和RBF神经网络的厦门市工程造价预测模型[J].华侨大学学报(自然科学版),2013,34(5):576-580.[doi:10.11830/ISSN.1000-5013.2013.05.0576]
　LIU Jing,YE Qing.Project Cost Prediction Model Based on BP and RBP Neural Networks in Xiamen City[J].Journal of Huaqiao University(Natural Science),2013,34(5):576-580.[doi:10.11830/ISSN.1000-5013.2013.05.0576]
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采用BP和RBF神经网络的厦门市工程造价预测模型()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第34卷
期数:
2013年第5期
页码:
576-580
栏目:
出版日期:
2013-09-20

文章信息/Info

Title:
Project Cost Prediction Model Based on BP and RBP Neural Networks in Xiamen City
文章编号:
1000-5013(2013)05-0576-05
作者:
刘婧;  叶青
华侨大学 土木工程学院, 福建 厦门 361021
Author(s):
LIU Jing;  YE Qing
College of Civil Engineering, Huaqiao University, Xiamen 361021, China
关键词:
工程估价;  预测模型;  多层前馈;  径向基函数;  神经网络;  厦门市
Keywords:
project cost estimation;  prediction model;  back propagation;  radial basis function;  neural network;  Xiamen City
分类号:
TU71
DOI:
10.11830/ISSN.1000-5013.2013.05.0576
文献标志码:
A
摘要:
收集55个厦门市典型工程造价指标,利用SPSS软件对数据进行预处理,选取11个工程特征作为造价的主要影响因素,分别建立基于多层前馈(BP)和径向基函数(RBF)神经网络的工程估价模型.从55个案例中随机抽取10个作为预测样本,剩下的45个作为训练样本,进行BP,RBF神经网络预测模型的训练和测试.结果表明:通过参数优选的RBF神经网络工程造价预测模型,预测误差在5%以内,网络泛化能力更优越,可用于实际工程造价的辅助估算.
Abstract:
By collecting 55 typical engineering cost indexes in Xiamen City and selecting 11 engineering feature cost per square meter as the main influencing factors, with the help of software SPSS, the neural network engineering cost estimation model was established based on back propagation(BP)and radial basis function(RBF). 10 cases in 55 cases were drew randomly as predicted sample, and the left 45 cases were taken as training sample, BP and RBF neural network prediction model were trained and tested. The results showed that the prediction error of RBF neural network through parameter optimization for project cost prediction model is within 5%, the network’s generalization ability is benign, so the model can be used for the actual project cost auxiliary estimation.

参考文献/References:

[1] 彭光金,俞集辉,韦俊涛,等.特征提取和小样本学习的电力工程造价预测模型[J].重庆大学学报,2009,32(9): 1105-1110.
[2] MANDAL P,SENJYU T,UEZATO K,et al.Several-hours-ahead electricity price and load forecasting using neural networks[C]//IEEE Power Engineering Society General Meeting.[s.l.]:IEEE Press,2005:2146-2153.
[3] 周双喜,郑智,鲁宗相.基于多种群遗传算法的规划[J].电力系统及自动化,2007,19(6):66-71.
[4] 罗楠,朱业玉,杜彩月.支持向量机方法在电力负荷预测中的应用[J].电网技术,2007,31(2):215-218.
[5] 李国勇.智能控制及其MATLAB实现[M].北京:电子工业出版社,2005:21-31.
[6] 马国丰,陈志琴.基于BP神经网络的轨道交通车站工程项目估价研究[J].项目管理技术,2011,9(7):55-60.
[7] 田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006:37-45.
[8] 满春涛,晓霞,张礼勇. 一种基于ACO的RBF神经网络训练方法[J].哈尔滨理工大学学报,2008,13(1):56-62.
[9] 叶青.厦门市工程估价的RBF神经网络预测模型[J].华侨大学学报:自然科学版,2012,33(4):446-450.

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备注/Memo

备注/Memo:
收稿日期: 2012-12-25
通信作者: 叶青(1968-),女,教授,主要从事建筑经济与项目管理的研究.E-mail:yeqing@hqu.edu.cn.
基金项目: 中央高校基本科研业务费专项资金资助项目(JB-ZR1162); 华侨大学高层次人才科研启动项目(12BS131)
更新日期/Last Update: 2013-09-20