Training Fuzzy Cognitive Maps by using Hebbian learning algorithms: A comparative study

Abstract

A detailed analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM so the model equilibrates to a desired state. For this manner, four different types of Hebbian learning algorithms have been proposed in the past. Along with the theoretical description of these algorithms, their performance in system modeling problems is investigated in this work. The algorithms are studied in a comparative fashion by using appropriate performance indices and useful conclusions about their training capabilities are experimentally derived.