Methods and algorithms for fuzzy cognitive map-based modeling

Abstract

The challenging problem of complex systems modeling methods with learning capabilities and characteristics that utilize existence knowledge and human experience is investigated using Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. Their usefulness has been proved from their wide applicability in diverse domains. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and ease of use. In general, they model the behavior of a complex system utilizing experts knowledge and/or available knowledge from existing databases. They are mainly used for knowledge representation and decision support where their modeling features and their learning capabilities make them efficient to support these tasks. This chapter gathers the methods and learning algorithms of FCMs applied to modeling and decision making tasks. A comprehensive survey of the current modeling methodologies and learning algorithms of FCMs is presented. The leading methods and learning algorithms, concentrated on modeling, are described analytically and analyzed presenting experimental results of a known case study. The main features of computational methodologies are compared and future research directions are outlined.