Short-Time Traffic Flow Prediction Using Fuzzy Wavelet Neural Network Based on Master-Slave PSO
Natural Computation, 2008. ICNC '08. Fourth International Conference（2008）
摘要
A particle swarm optimization (PSO) algorithm with master-slave structure is proposed to train fuzzy wavelet neural network which be used to predict short-time traffic flow. The PSO algorithm is formulated in a form of hierarchical structure. The global search is performed at the master level, while the local search is carried out at the slave level. Through the harmonizing mechanism between master and slave level, the algorithm can execute global exact search without relying on complex coding operators. The simulation results demonstrate the proposed model can improve prediction accuracy, compared with BP based training techniques.
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关键词
pso algorithm,fuzzy set theory,slave level,fuzzy wavelet neural network,global exact search,complex coding operators,prediction model,wavelet transforms,particle swarm optimisation,short-time traffic flow,master level,global search,complex coding operator,hierarchical structure,master-slave pso,particle swarm optimization algorithm,local search,road traffic,master-slave structure,fuzzy neural nets,short-time traffic flow prediction,prediction algorithms,fuzzy control,predictive models,master slave,artificial neural networks,traffic flow
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