摘要
针对传统建筑工程造价估算方法耗时量大、计算繁琐、误差频出的问题,提出了一种用自组织特征映射(SOFM)网络对建筑工程量样本量化后的值进行聚类的方法。该方法不需要手动标识训练数据集就可以实现不同类型的建筑样本自动分类,有助于提高传统建筑工程造价估算的效率。最后,通过实例验证了该方法的实用性和有效性。实验结果表明,改进的方法用于建筑工程造价估算较传统方法而言具有更高的准确率和更低的误报率。
The traditional project cost estimation in architecture has many problems such as huge time-consumption,complicated calculation,and frequent measurement error.Therefore,a method of clustering which could deal with architecture samples by Self-Organizing Feature Map （SOFM） network was proposed.This method did not need to identify training data set manually to get classification from different sorts of samples,and it did help to improve the efficiency of the traditional architectural project cost estimation.Finally,the availability of the algorithm in this method was proved.Compared with the traditional methods,the experimental results demonstrate that the improved method has a higher accuracy rate and a lower false positive rate.
出处
《计算机应用》
CSCD
北大核心
2010年第6期1543-1546,1576,共5页
journal of Computer Applications
基金
国家"十一五"科技支撑计划项目(2007BAF23B0505)
关键词
工程造价估算
神经网络
自组织特征映射
建筑施工
特征
project cost estimation
neural network
Self-Organizing Feature Map （SOFM）
building construction
feature
