Learning and Convergence of Fuzzy Cognitive Maps Used in Pattern Recognition

In recent years fuzzy cognitive maps (FCM) have become an active research field due to their capability for modeling complex systems. These recurrent neural models propagate an activation vector over the causal network until the map converges to a fixed-point or a maximal number of cycles is reached. The first scenario suggests that the FCM converged, whereas the second one implies that cyclic or chaotic patterns may be produced. The non-stable configurations are mostly related with the weight matrix that defines the causal relations among concepts. Such weights could be provided by experts or automatically computed from historical data by using a learning algorithm. Nevertheless, from the best of our knowledge, population-based algorithms for FCM-based systems do not include the map convergence into their learning scheme and thus, non-stable configurations could be produced. In this research we introduce a population-based learning algorithm with convergence features for FCM-based systems used in pattern classification. This proposal is based on a heuristic procedure, called Stability based on Sigmoid Functions, which allows improving the convergence of sigmoid FCM used in pattern classification. Numerical simulations using six FCM-based classifiers have shown that the proposed learning algorithm is capable of computing accurate parameters with improved convergence features.