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.

Keywords

• fuzzy cognitive maps
• learning algorithms
• fuzzy cognitive map learning,
• evolutionary learning