Learning Fuzzy Cognitive Maps by a Hybrid Method Using Nonlinear Hebbian Learning and Extended Great Deluge Algorithm

Fuzzy Cognitive Maps (FCM) is a technique to represent models of causal inference networks. Data driven FCM learning approach is a good way to model FCM. We present a hybrid FCM learning method that combines Nonlinear Hebbian Learning (NHL) and Extended Great Deluge Algorithm (EGDA), which has the efficiency of NHL and global optimization ability of EGDA. We propose using NHL to train FCM at first, in order to get close to optimization, and then using EGDA to make model more accurate. We propose an experiment to test the accuracy and running time of our methods. Fuzzy Cognitive Maps (FCM) (1) is a modeling methodology that represents graph causal relations o