Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification

Abstract

After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

A review on methods and software for fuzzy cognitive maps

“…FCM Tool eventually evolved into FCM Expert [76], a general-purpose and more complete software platform for modeling FCM-based systems. As mentioned, FCM Tool was meant to address a specific decision-making problem.…”

Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

“…In the problem of improvement strategy selection, fuzzy AHP is used to weight criteria, and fuzzy TOPSIS is used to rank the strategies based on the criteria. FCM is a causal cognition tool for modeling and simulating dynamic systems [35]. In this study, FCM is utilized to quantify the impact of strategies on CLP by modeling a CLP environment.…”

Fuzzy Cognitive Maps-Based Switched-Mode Power Supply Design Assistant System

“…FCMs were invented by B. Kosko as a knowledge-based methodology for modeling and simulating dynamic systems in order to improve decision maker's ability to understand the dynamic behavior of causal cognitive maps [19]. FCMs are inference networks using cyclic digraphs, which originated from the combination of fuzzy logic and neural networks for knowledge representation and reasoning [17].…”

“…(2) W : (C i , C j ) → w ij is a function of n × n to a pair of concepts (C i , C j ) taking value in the range of −1 to 1, with w ij denoting a weight of directed edge from C i to C j . W (n×n) = (w ij ) is a connection or edge matrix [19]. There are three possible types of causal relations between concepts: (a) If w ij > 0 , there is a positive causality, which means increasing C i leading to an increase in the C j with intensity w ij .…”