کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382611 660772 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
چکیده انگلیسی

In this paper, a hybrid approach to combine conditional restricted Boltzmann machines (CRBM) and echo state networks (ESN) for binary time series prediction is proposed. Both methods have demonstrated their ability to extract complex dynamic patterns from time-dependent data in several applications and benchmark studies. To the authors’ knowledge, it is the first time that the proposed combination of algorithms is applied for reliability prediction.The proposed approach is verified on a case study predicting the occurrence of railway operation disruptions based on discrete-event data, which is represented by a binary time series. The case study concerns speed restrictions affecting railway operations, caused by failures of tilting systems of railway vehicles. The overall prediction accuracy of the algorithm is 99.93%; the prediction accuracy for occurrence of speed restrictions within the foresight period is 98% (which corresponds to the sensitivity of the algorithm). The prediction results of the case study are compared to the prediction with a MLP trained with a Newton conjugate gradient algorithm. The proposed approach proves to be superior to MLP.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 40, Issue 15, 1 November 2013, Pages 6033–6040
نویسندگان
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