Research and Application of PSO-Based BP Neural Network in the Project Estimate of Government Investment

Author

Liu, Wenhui ; Chi, Zhifeng

Author_Institution

Dept. of Inf. & Technol., Kashgar Teachers´´ Coll., Kashgar, China

fYear

2009

fDate

11-12 July 2009

Firstpage

257

Lastpage

260

Abstract

In order to increase the accuracy of the cost estimates in government investment, a method based on the PSO trained neural network to estimate the cost is proposed. First the neural network model of a project cost estimate is created, and then PSO is introduced to optimize the weight and threshold of the neural network, at last the neural network trained is used to estimate cost of the project. The results show that this method not only overcome the defects of traditional neural network such as learning for a long time and shock, but also increase the accuracy of the cost of the project to estimate.

Keywords

costing; investment; neural nets; particle swarm optimisation; public finance; PSO trained BP neural network; government investment; particle swarm optimization algorithm; project cost estimation; Artificial neural networks; Biological neural networks; Cost function; Feedforward neural networks; Government; Investments; Multi-layer neural network; Neural networks; Particle swarm optimization; Signal processing algorithms; BP neural network; Cost estimates; Optimization; PSO algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-0-7695-3728-3

Type

conf

DOI

10.1109/CASE.2009.45

Filename

5194440