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

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  • Zhineng Hu
  • Yixin Zhang
  • Liming Yao

Abstract

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.

Suggested Citation

  • Zhineng Hu & Yixin Zhang & Liming Yao, 2014. "Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-13, November.
    • Handle: RePEc:hin:jnddns:414058
    DOI: 10.1155/2014/414058
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    Cited by:

    1. Sijing Liu & Jiuping Xu & Xiaoyuan Shi & Guoqi Li & Dinglong Liu, 2018. "Sustainable Distribution Organization Based on the Supply–Demand Coordination in Large Chinese Cities," Sustainability, MDPI, vol. 10(9), pages 1-25, August.

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