Abstract: Modeling of complex dynamic systems, which are
very complicated to establish mathematical models, requires new and
modern methodologies that will exploit the existing expert
knowledge, human experience and historical data. Fuzzy cognitive
maps are very suitable, simple, and powerful tools for simulation and
analysis of these kinds of dynamic systems. However, human experts
are subjective and can handle only relatively simple fuzzy cognitive
maps; therefore, there is a need of developing new approaches for an
automated generation of fuzzy cognitive maps using historical data.
In this study, a new learning algorithm, which is called Big Bang-Big
Crunch, is proposed for the first time in literature for an automated
generation of fuzzy cognitive maps from data. Two real-world
examples; namely a process control system and radiation therapy
process, and one synthetic model are used to emphasize the
effectiveness and usefulness of the proposed methodology.