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- W2378619900 abstract "Method named BAYESIAN combined neural network model is proposed for short term traffic flow prediction in this paper. Such a model tracks the prediction performance of the combined models, and adjusts their credit values according to a proposed set of assignment algorithm so as to select the models with higher accuracy for combination. The basis theory of this model and its application in a test network are expounded. Back propagation (BP) neural network and radial basis function (RBF) neural network are selected to construct the combined model, which are applied to a test data set for performance comparison. It is found that the performances of combined model are better than those of the singular neural network model. More important, it is the characteristic of the combined model to track prediction model performance online and to always combine the better performing predictors for a stable prediction." @default.
- W2378619900 created "2016-06-24" @default.
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- W2378619900 date "2005-01-01" @default.
- W2378619900 modified "2023-09-23" @default.
- W2378619900 title "Study of short-term freeway traffic flow prediction based on BAYESIAN combined model" @default.
- W2378619900 hasPublicationYear "2005" @default.
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