NDLI: A travel time prediction method: Bayesian reasoning state-space neural network

Content Provider	IEEE Xplore Digital Library
Author	Xingyi Li Cunqing Wang Huaji Shi
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China (Xingyi Li; Cunqing Wang; Huaji Shi)
Abstract	According to the prediction model of neural network training methods to slow convergence speed, training for a long time and difficult to control the complexity of weights updating, this paper puts forward Bayesian reasoning state-space neural network, using termination conditions control its training and the confidence interval restrained by control factor standard the results. Using this method can accelerate convergence, shorten the training time and maintain stability. With traffic data by Bin He road of Shenzhen in September 2007 to verify this model, the experiments show that this model can shorten the time of training, and has good robustness and accuracy.
Starting Page	936
Ending Page	940
File Size	273891
Page Count	5
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5689258
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Travel time prediction Termination conditions Artificial neural networks Predictive models Stability analysis Confidence interval Training Accuracy Bayesian methods Hyper-parameters Data models Bayesian inference State-space neural network
Content Type	Text
Resource Type	Article

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