Abstract: Fuzzy cognitive map (FCM) is an effective tool for modeling of dynamic decision support systems.
The crucial issue connected with the FCM is the ability to learn the relations matrix based on real data.
This paper presents the use of fuzzy cognitive map and multi-step learning algorithms in modeling of decision
support system for traffic forecasting. FCM and multi-step supervised learning algorithms based on gradient
method are described. Selected results of simulation analysis of the cognitive modeling on the example
of traffic forecasting are shown. FCM learning and testing were performed with the use of real normalized
data. Comparative analysis of multi-step gradient method to one-step algorithm, from the point of view
of the influence on the modeled system was done. The results show the sufficient effectiveness of the use
of fuzzy cognitive map and multi-step learning algorithms in traffic forecasting.
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