Paper Info
Title
Research and Application of Urban Logistics Demand Forecast Based on High Speed and Precise Genetic Algorithm Neural Network
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.
Year
DOI
Venue
2009
10.1007/978-3-642-01510-6_63
ISNN (2)
Keywords
Field
DocType
convergence speed,network structure,algorithm flow,urban logistics demand,genetic algorithm,urban logistics,precise genetic algorithm neural,high speed,bp network,urban logistics system,demand forecasting,forecast,indexation,floating point,neural network
Convergence (routing),Mathematical optimization,Genetic algorithm neural network,Nonlinear system,Demand forecasting,Simulation,Computer science,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm,Network structure
Conference
Volume
ISSN
Citations 
5552
0302-9743
1
PageRank 
References 
Authors
0.44
1
3
Name
Order
Citations
PageRank
Jingwen Tian13613.10
Meijuan Gao23212.32
Fan Zhang3427.62