Research and Application of Urban Logistics Demand Forecast Based on High Speed and Precise Genetic Algorithm Neural Network

  • Authors:

  • Jingwen Tian;Meijuan Gao;Fan Zhang

  • Affiliations:

  • College of Automation, Beijing Union University, Beijing, China 100101 and School of Information Science, Beijing University of Chemical Technology, Beijing, China 100029;College of Automation, Beijing Union University, Beijing, China 100101 and School of Information Science, Beijing University of Chemical Technology, Beijing, China 100029;School of Information Science, Beijing University of Chemical Technology, Beijing, China 100029

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
  • Year:
  • 2009

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Abstract

Considering the issues that the urban logistics system is an uncertain, nonlinear, dynamic and complicated system, and it is difficult to describe it by traditional methods, an urban logistics demand forecast method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The main parameters of affecting urban logistics demand are studied. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the forecast method can truly forecast the urban logistics demand by learning the index information of affect urban logistics demand. The actual forecasting results show that this method is feasible and effective.