Cost Forecasting Model of Transmission Project based on the PSO-BP Method
Abstract: In order to solve
being sensitive to the initial weights, slow convergence, being easy to fall
into local minimum and other problems of the BP neural network, this paper
introduces the Particle Swarm Optimization (PSO) algorithm into the Artificial
Neural Network training, and construct a BP neural network model optimized by
the particle swarm optimization. This method can speed up the convergence and
improve the prediction accuracy. Through the analysis of the main factors on
the cost of transmission line project, dig out the path and lead factors,
topography and meteorological factors, the tower and the tower base materials
and other factors. Use the PSO-BP model for the cost forecasting of
transmission line project based on historical project data. The result shows
that the method can predict the cost effectively. Compared with the traditional
BP neural network, the method can predict with higher accuracy, and can be
generalized and applied in cost forecasting of actual projects.
Author: Yan Lu, Dongxiao Niu,
Bingjie Li, Min Yu
Journal Code: jptkomputergg140112
