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Investigation of the chaotic behavior of traffic streams at urban intersections due to signals has involved researchers in endeavoring to predict a smooth traffic flow model for stabilizing traffic congestion and avoid unnecessary delays. In this paper, we study a hybrid adaptive model, based on a combination of coloured Petri nets, fuzzy logic and learning automata, to efficiently control traffic...
Increasing number of vehicles, as the natural consequence of population growth, has caused a significant bottle-neck in transportation network and consequently major delays at intersections. Hence, in this paper we study a hybrid adaptive model, based on combination of Coloured Petri Nets, Fuzzy Logic and Learning Automata to efficiently control traffic signals. We show that in comparison with the...
This paper presents the application of artificial neural networks to implement a magnetic hysteresis model. Accurate modelling of hysteresis is essential for both the design and the performance evaluation of electromagnetic devices. It is shown that artificial neural networks (ANNs) provide natural settings whereby the Preisach model can be readily implemented. The comparison with the experiments...
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