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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 414058, 13 pages
Research Article

Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction

Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, China

Received 26 June 2014; Revised 8 September 2014; Accepted 4 November 2014; Published 26 November 2014

Academic Editor: Zhigang Jiang

Copyright © 2014 Zhineng Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Regional logistics prediction is the key step in regional logistics planning and logistics resources rationalization. Since regional economy is the inherent and determinative factor of regional logistics demand, it is feasible to forecast regional logistics demand by investigating economic indicators which can accelerate the harmonious development of regional logistics industry and regional economy. In this paper, the PSO-RBFNN model, a radial basis function neural network (RBFNN) combined with particle swarm optimization (PSO) algorithm, is studied. The PSO-RBFNN model is trained by indicators data in a region to predict the regional logistics demand. And the corresponding results indicate the model’s applicability and potential advantages.