Concept by Concept Learning of Fuzzy Cognitive Maps

Abstract : Fuzzy cognitive maps (FCM) are fuzzy signed directed graphs with feedbacks; they are simple and powerful tool for simulation and analysis of complex, nonlinear dynamic systems. However, FCM models are created by human experts mostly, and so built FCM models are subjective and building a FCM model becomes harder as number of variables increases. So in the last decade several methods are proposed providing automated generation of fuzzy cognitive maps from data. The main drawback of the proposed automated methods is their weaknesses on handling with large number of variables. The proposed method brings out a new strategy called concept by concepts approach (CbC) approach for learning of FCM. It enables the generation of large sized FCM models with a high precision and in a rapid way using the historical data.
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Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.577-586, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_58〉
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M. Dodurka, Engin Yesil, Cihan Ozturk, Ahmet Sakalli, Cagri Guzay. Concept by Concept Learning of Fuzzy Cognitive Maps. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.577-586, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_58〉. 〈hal-01459649〉

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