Computational Technology Resources - CCC

Z. Huang¹, X. Cai¹, R. Jin¹, J. Sun², F. Wang², B. Liu³ and M. Dai³

¹School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
²China Academy of Railway Sciences, Beijing, China
³Guangdong Sugo Technology Corporation Limited, China

doi:10.4203/ccc.7.23.3

Z. Huang, X. Cai, R. Jin, J. Sun, F. Wang, B. Liu, M. Dai, "Railway Material Forecasting With Particle Swarm Optimization-Based Neural Network", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 23.3, 2024, doi:10.4203/ccc.7.23.3

Keywords: railway material, prediction model, particle swarm optimization, particle swarm optimization-based neural network, validation, forecasting.

Abstract

Railway material management represents a significant challenge in the daily operations of railway departments. Accurate and efficient prediction of railway material demand holds substantial practical and academic significance. This paper, based on a large amount of real railway material data provided by one large railway company in China, constructs a prediction model using a particle swarm optimization-based neural network to forecast railway material demand. The study finds that the particle swarm optimization-based neural network model not only possesses a high prediction accuracy but also exhibits strong generalization capabilities, making it particularly suitable for railway material data characterized by high discreteness and randomness.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 23.3 Railway Material Forecasting With Particle Swarm Optimization-Based Neural Network Z. Huang¹, X. Cai¹, R. Jin¹, J. Sun², F. Wang², B. Liu³ and M. Dai³ ¹School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China ²China Academy of Railway Sciences, Beijing, China ³Guangdong Sugo Technology Corporation Limited, China doi:10.4203/ccc.7.23.3 Full Bibliographic Reference for this paper Z. Huang, X. Cai, R. Jin, J. Sun, F. Wang, B. Liu, M. Dai, "Railway Material Forecasting With Particle Swarm Optimization-Based Neural Network", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 23.3, 2024, doi:10.4203/ccc.7.23.3 Keywords: railway material, prediction model, particle swarm optimization, particle swarm optimization-based neural network, validation, forecasting. Abstract Railway material management represents a significant challenge in the daily operations of railway departments. Accurate and efficient prediction of railway material demand holds substantial practical and academic significance. This paper, based on a large amount of real railway material data provided by one large railway company in China, constructs a prediction model using a particle swarm optimization-based neural network to forecast railway material demand. The study finds that the particle swarm optimization-based neural network model not only possesses a high prediction accuracy but also exhibits strong generalization capabilities, making it particularly suitable for railway material data characterized by high discreteness and randomness. download the full-text of this paper (PDF, 3079 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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