Prediction of the optimal hybrid train trajectory by using artificial neural network models

Din, Tajud, Tian, Zhongbei ORCID: 0000-0001-7295-3327, Bukhari, Syed Muhammad Ali Mansur, Din, Misbahud, Hillmansen, Stuart and Roberts, Clive (2024) Prediction of the optimal hybrid train trajectory by using artificial neural network models. IET Intelligent Transport Systems, 18 (5). pp. 835-852.

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Official URL: http://dx.doi.org/10.1049/itr2.12472

Abstract

<jats:title>Abstract</jats:title><jats:p>This paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models’ performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%).</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Patient Safety, 7 Affordable and Clean Energy
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	24 Jan 2024 11:06
Last Modified:	06 May 2024 02:39
DOI:	10.1049/itr2.12472
Open Access URL:	https://doi.org/10.1049/itr2.12472
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177978