This paper deals with a way of applying a neural networkfor describing se1vice station load in a maintenance unit. Dataacquired by measuring the workload of single stations in amaintenance unit were used in the process of training the neuralnetwork in order to create a model of the obse1ved system.The model developed in this way enables us to make more accuratepredictions over critical overload. Modelling was realisedby developing and using m-functions of the Matlab software.
C. J, Haris, Advances in Intelligent Control. Taylor&
Francis Ltd., London, 1994.
H. Gold, Neuronske mreie u prometu i transportu (Neural
networks in Traffic and Transport), Authorised Lectures.
FPZ, Zagreb, 1998. (in Croatian)
H. Gold, Neuronske mreie u prometu i transportu, Upute
za vjetbe na racunalu. FPZ, Zagreb, 1998. (in Croatian)
D.P. Pham, X. Liu, Neural networks for identification,
prediction and control. Springer-Verlag, London, 1995.
V. Ceric, Simulacijsko modeliranje, Skolska knjiga, Zagreb,
(in Croatian)
B. Novakovic, Umjetna inteligencija i proizvodni sustavi.
FSB, Zagreb, 1998. (in Croatian)
P. Sikavica, H. Skoko, D. Tipuric, M. Dalic, Poslmmo
odlucivanje. Informator, Zagreb 1994. (in Croatian)
J, L. McClelland, D. E. Rumelhart, Explorations in
Parallel Distributed Processing, Bradford Book, Cambridge,
H. Demuth, M. Beale, Neural Network Toolbox For Use
with Matlab, User's guide, Math Works, 1992.
V. Ziljak, Modeliranje i simuliranje. Skolska knjiga,
Zagreb 1982. (in Croatian)
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal