nonlinear hebbian algorithm

This article is an investigation of some problems of modeling, expression of characteristics and analysis of Complex Dynamic Systems (CDS) using Fuzzy Cognitive Maps (FCM). Efficient analysis and control of CDS is impossible without a formal model of the system. The key problem of CDS and control theory is to develop methods to qualitatively analyze the dynamics and behavior of such systems and to construct efficient control algorithms for their efficient operation. Most qualitative description of the parameters of complex dynamic systems inevitably leads to ambiguity, complexity and uncertainty. Reasons for using Fuzzy Cognitive Maps (FCM) in CDS modeling are presented. FCMs provide an opportunity to generate new knowledge based on system applications and address the need to address the uncertainties, ambiguities, and inaccuracies associated with real CDS problems. In this study, the basic principles of FCM are briefly presented and then the NHL algorithm for training the FCM model and adjusting these weights. With fuzzy expressions and converting qualitative data into quantitative, a presentation and an example about the factors affecting the phenomenon of divorce were presented. The example results are promising, further development of CDS modeling and control using soft computing methods.