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- W2364331408 abstract "Based on the braking characteristics of train station parking in urban rail transit,two simplifications about the train dynamics equation were made:one was neglecting the influence caused by the air resistance and the gradient,the other was assuming that there was a braking delay and a constant deceleration or the deceleration was a linear function of the initial speed.Then,two simplified models were developed to estimate the train station parking error.Furthermore,the linear relationships between the parameters of the two models were induced and the online learning algorithm for updating the models'parameters was developed to overcome the influence by the nonlinear factors in parking to increase the parking accuracy.According to the statistics principle,five evaluation indices were adopted to evaluate the performance of the models and the parking error estimation was adopted to judge whether the parking error could meet the requirements of parking reliability.The field train parking data were used to validate and compare the performance of the models and the online learning algorithm.The results show that the proposed simplified models and the online learning algorithms are effective in reducing the parking error and correct the bias of the error distribution.When the parking error is greater than 99.5%,it can meet the 30cm reliabil-ity requirements for the parking accuracy.Model 1achieves better performance than model 2." @default.
- W2364331408 created "2016-06-24" @default.
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- W2364331408 date "2010-01-01" @default.
- W2364331408 modified "2023-09-24" @default.
- W2364331408 title "Research on the Error Estimation Models and Online Learning Algorithms for Train Station Parking in Urban Rail Transit" @default.
- W2364331408 hasPublicationYear "2010" @default.
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