Algorithm for MCDM in Intelligent Braking Diagnostics System of Railway Transport

2011
Anatolijs Ļevčenkovs, Mihails Gorobecs, Leonīds Ribickis, Peteris Balckars

This paper is devoted to modelling of intelligent braking diagnostics system of railway transport. Nowadays human factor plays a significant role in control of railway system in a whole and a rolling stock in particularly. The task is to prevent such accidents by reducing the human factor. In this paper artificial neural network controller, mathematical models and clustering algorithm for task solution is proposed. Results of experiment show the possibility to use neural network controller for speed control of DC drive depending on the distance to stop. The results show the possibility of the developed systems to prevent accidents and to avoid different problems by intelligent diagnostic and coordination devices. Neural network may be used to prevent breakdowns and accidents and such kind of controllers may be integrated in working infrastructure for optimal speed control of railway traffic.

Atslēgas vārdi
decision-making, clustering algorithms, scheduling theory, intelligent electric transport system
DOI
10.1007/978-3-642-19695-9_12
Hipersaite
http://link.springer.com/chapter/10.1007%2F978-3-642-19695-9_12

Ļevčenkovs, A., Gorobecs, M., Ribickis, L., Balckars, P. Algorithm for MCDM in Intelligent Braking Diagnostics System of Railway Transport. No: New State of MCDM in the 21st Century: Selected Papers of the 20th International Conference on Multiple Criteria Decision Making 2009. Berlin: Springer-Verlag, 2011. 143.-156.lpp. ISBN 978-3-642-19695-9. ISSN 0075-8442. Pieejams: doi:10.1007/978-3-642-19695-9_12

Publikācijas valoda
English (en)