A Review of Fuzzy Cognitive Maps Extensions and Learning

Abstract

Fuzzy Cognitive Maps (FCM) is a soft computing technique whose vertices and edges are fuzzy values with an inference mechanism for solving modelling problems; it has been used in modelling complex systems like industrial and process control. The concept was first introduced in 1986, with an initial learning algorithm in 1996; several works have been published on FCM methodology, learnings and applications. Fuzzy cognitive maps continue to evolve both in theory, learning algorithms and application. Many theories like intuitionistic theory, hesitancy theory, grey system theory, wavelet theory, etc., are integrated with the conventional FCM. These extensions have improved Fuzzy cognitive Maps to handle problems of uncertainty, incomplete information, hesitancy, dynamic systems and probabilistic fuzzy events. They also strengthen fuzzy cognitive Maps’ modelling power for application in almost any domain. However, the compilation of the development in methodology and adaptation of FCM are either old or omitted some of the recent advances or focused on specific applications of FCM in some areas. This paper reports extension, learning and applications of FCM from the initial conventional FCM to recent extensions and some of the important features of those extensions and learning.

Fuzzy cognitive mapping in participatory research and decision making: a practice review

“…Our experiences with FCM resonate with and adds considerable detail to earlier FCM authors [ 18 , 19 ], including those offering protocols for meaningful participation in environmental sciences [ 16 , 49 ]. The most recent literature reviews on the use of FCM have not discussed the contributions we described here [ 20 , 22 , 55 , 56 ] and do not provide details on practical decisions across the mapping process or on the implications of stakeholder authorship. This review provides practical insights for FCM researchers before they generate maps, during data collection and in analysis.…”

“…Decision-making involves choosing alternatives based on their expected impacts. Many people think of FCM in the context of predictive models using learning algorithms [ 55 , 56 , 69 – 72 ]. There is also potential for informing other AI-driven methods by incorporating expert knowledge in the form of fuzzy cognitive maps into complex graph-based models [ 73 , 74 ].…”