Traffic flow prediction method based on corrosion de-noising deep belief network
A technology of deep belief network and prediction method, which is applied in the field of traffic flow prediction based on corrosion and denoising deep belief network, can solve the problem of over-fitting problem that has not been solved well, so as to improve the generalization ability, relieve traffic pressure, and reduce traffic flow. risk effect
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[0030]The present invention utilizes the method of deep learning to transform the traditional deep belief network to further obtain more representative features, improve the generalization ability of the model, and effectively alleviate the problem of overfitting. The traditional deep belief network is constructed by stacked restricted Boltzmann machines. The innovation of the present invention is that a random corrosion layer is added to the input of each restricted Boltzmann machine during training, and the random corrosion layer The output of the layer is used as the new visible layer, and the hidden layer does not change.
[0031] Corrosion probability is a global hyperparameter. The smaller the corrosion probability, the more neurons are retained. When the corrosion probability is 0, the stochastic corrosion layer degenerates into an ordinary identity mirror layer, and the output simply copies the input; the larger the corrosion probability, the more neurons are lost Act...
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