Fuzzy Cognitive Map Reconstruction - Methodologies and Experiments

The paper is focused on fuzzy cognitive maps - abstract soft computing models, which can be applied to model complex systems with uncertainty. The authors present two distinct methodologies for fuzzy cognitive map reconstruction. Both theoretical and practical issues involved in the process of a map reconstruction are discussed. Among researched and described aspects are: map sizes, data dimensionality, distortions, optimization procedure, etc. Theoretical results are supported by a series of experiments, that allow to evaluate the quality of the developed approach. The authors compare both procedures characteristics and discuss practical issues, that are entailed in the developed methodology. The goal of this study is to investigate theoretical and practical problems, that are relevant in the fuzzy cognitive map reconstruction process. Proposed two methodologies for FCM reconstruction are based on gradient learning. A series of experiments allows to illustrate important characteristics of the fuzzy cognitive map reconstruction procedure.