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The paper compares the performances between the back propagation (BP) neural network and the radial basis function (RBF) neural network in chaotic time series prediction with the Logistic equation, and the results show that the RBF neural network is better than the BP neural network. Further we apply the RBF neural network to predict the Shanghai Composite index that is chaotic according to the phase...
Considering the issues that the urban logistics system was an uncertain, nonlinear, dynamic and complicated system, and it was difficult to describe it by traditional methods, an urban logistics demand forecast method based on radial basic function neural network (RBFNN) is presented in this paper. We construct the structure of RBFNN that used for forecasting urban logistics demand, and adopt the...
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