查看更多>>摘要：The construction of an accurate train control model (TCM) is crucial to the design of automatic train operation and real-time traffic management systems in high-speed railways. Traditional physical-driven models usually fail to reflect the "true" dynamics of high-speed trains (HSTs) because of the strong nonlinearity and uncertainty due to air resistance, frequently switching working conditions, and variations in external influencing factors such as weather and temperature. Although some data-driven deep learning models have recently been proposed for environmental adaptation, they are all "black box" models, which cannot explain how the input of the models affects the HST output. To overcome these issues, this study constructs a novel long short-term memory with lagged information (LAG-LSTM) model by combining the physical-driven HST model and an "interpretable" deep learning model. Specifically, our LAG-LSTM model contains three modules: a time-delay variable module to model the transform delay of control variables, state variable enhancement module to extract the key features among high dimensional input data, and Pre-LSTM module to predict the future train trajectory with given control variables. We collected field data from the Beijing-Shanghai high-speed railway and developed a data filter method and a normalization procedure to overcome the positioning errors of HSTs and construct a standard data set. Finally, we tested the effectiveness of our LAG-LSTM by comparing it with six deep learning structures, including fully connected neural network, recursive neural network, standard LSTM, and LSTM with convolutional layers. The results show that LAG-LSTM can accurately predict the trajectories of HSTs and outperforms other deep learning models. Regarding prediction accuracy, LAG-LSTM improved the performance of the traditional LSTM by 13.5% to 23.3%. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In granular computing, a set of attributes (features) is often selected as a view to describe objects from a particular angle. In each view, objects can be further described from different levels of granularities (abstraction), and each granularity determines a level. Multiview and multilevel are two basic principles of granular computing, which render a solution more comprehensive and reasonable. From the viewpoint of granular computing, existing three-way decisions cannot effectively combine multiview and multilevel to make decisions. As a new granular computing model, the partition order product space solves a problem from multiple views and at multiple levels in each view, which follows the principles of multiview and multilevel. In this paper, we discuss three-way decisions based on partition order product space. First, we propose two search algorithms: depth-first and breadth first searching algorithms, to obtain a solution for problem solving in partition order product space. Second, we introduce two fusion strategies to fuse multiple one-level views: optimistic fusion method and pessimistic fusion method. Consequently, based on two search algorithms and two fusion strategies, we propose four multiview sequential three-way decisions, which can simultaneously make decisions from multiple views and multiple levels. Experimental results demonstrate the effectiveness of the proposed models. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The selection of hyper-parameters in building neural networks is often left to end-users. However, this may lead to poor performance regardless of the amount of experience of the user. With deep implementation, this becomes even more challenging due to a large number of hyper-parameters. Deep stochastic configuration networks (DeepSCNs) have become increasingly popular over the past years because of their universal approximation property, fast learning and easy implementation. So far, understanding and setting the hyper-parameters for this type of network is still unexplored. This paper defines a suitable search space for DeepSCN, a performance estimation strategy for finding suitable hyper parameters with search strategies Monte-Carlo tree search (MCTS) and random search. Simulations are performed using both searches over four benchmark datasets, and results indicate some significant improvements in modelling performance. Furthermore, a case study is presented to demonstrate that an optimised model and hyper-parameters can be found using MCTS.(c) 2022 Published by Elsevier Inc.
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