Forecasting the cost of municipal engineering based on PCA and NARX neural network

Date

2017-12-20

Advisors

Journal Title

Journal ISSN

ISSN

1671-7848

DOI

Volume Title

Publisher

Type

Article

Peer reviewed

Yes

Abstract

There are many factors influencing the cost of municipal engineering. It is usually difficult and time consuming to predict the cost of municipal engineering. This paper proposes a two-phase method to predict the cost of municipal engineering by combining principal component analysis (PCA) and NARX neural network. In the first phase of the proposed method, PCA is used to analyze the correlations among involved factors based on the original data and extract key factors that influence the cost of municipal engineering. In the second phase, taking the key factors extracted by PCA as input and the unit area engineering cost as the output, a NARX neural network model is constructed based on the Bayesian regularization algorithm to predict the cost of municipal engineering. The experimental results show that the proposed method can predict the cost of municipal engineering fast and accurately, which shows that the proposed prediction method is feasible and effective.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Municipal engineering, Forecasting, Principal component analysis, Neural network

Citation

Zhang, X. and Yang, S. (2018) Forecasting the cost of municipal engineering based on PCA and NARX neural network. Control Engineering of China, 24(12), pp. 2485-2490.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)