Abstract
In this work, we use the kosko’s fuzzy cognitive maps to represent the reasoning mechanism in complex dynamic systems. The proposed approach focuses on two points: the first one is to improve the learning process by providing a connection between Kosko’s FCMs and reinforcement learning paradigm, and the second one is to diversify the states of FCM concepts by using an IF-THEN rules base based on the Mamdani-type fuzzy model. An important result is the creation of the transition maps between system states for helpful knowledge representation. After transition maps are validated, they are aggregated and merged as a unique map. This work is simulated under Matlab with Fuzzy Inference System Platform.
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Tlili, A., Chikhi, S., Abraham, A. (2021). Traveler Behavior Cognitive Reasoning Mechanism. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_17
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DOI: https://doi.org/10.1007/978-3-030-49345-5_17
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