电力工程造价的随机权深度神经学习估算方法

Random weighted deep neural learning based cost estimation of power engineering project

为了实现对电力工程造价高效、精确的估算,提出了一种电力工程造价的随机权深度神经学习估算算法(Random Weighted Deep Neural Learning,RWDNL)。通过构建外权随机的带有小中间层的多隐层神经网络模型,利用神经网络深度学习实现了对海量数据有效特征的提取以及电力工程项目造价估算。数值仿真实验结果表明该方法使工程造价估算精度和速度大大提高,可获得令人满意的泛化能力。

In order to realize an efficient and accurate cost estimation of power engineering project, a cost estimation method for power engineering project with big data is proposed based on a so-called Random Weighted Deep Neural Learning（RWDNL）algorithm. By means of building a multi-layer random weighted neural network with a small central layer, effective features are extracted from mass engineering data and power engineering project cost estimation is also real-ized by neural network deep learning. The experimental results demonstrate the outstanding performances of the proposed RWDNL method on estimation time consume and estimation accuracy, as well as its satisfactory generalization ability.