Gradient-Based Algorithms for the Automatic Construction of Fuzzy Cognitive Maps

Fuzzy Cognitive Map (FCM) is a tool for modeling and representing discrete dynamical systems. Several approaches were proposed for the automatic learning of FCM on the basis of historical data. The learning techniques can be grouped into three types: Hebbian-based, population-based, and hybrid, which combines both types. Despite the good overall results achieved by population-based approaches relative to the other learning paradigms, it is possible to improve their performance by combining them with local search procedures. In this paper, we investigate the performance of a multi-start gradient-based method and two evolutionary methods hybridized with a gradient-based local search procedure for the learning of FCMs. We tested the proposed approaches for synthetic and real world FCM models. The results show that it was possible to improve the performance of the evolutionary methods with a relatively small increase in the resultant computational time.