International Journal of Man-Machine Studies
Introduction to Grey system theory
The Journal of Grey System
Fuzzy engineering
Reasoning and unsupervised learning in a fuzzy cognitive map
Information Sciences—Informatics and Computer Science: An International Journal
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
A fuzzy cognitive map approach for effect-based operations: An illustrative case
Information Sciences: an International Journal
Benchmarking main activation functions in fuzzy cognitive maps
Expert Systems with Applications: An International Journal
Augmented fuzzy cognitive maps for modelling LMS critical success factors
Knowledge-Based Systems
A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Applied Soft Computing
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
Modelling grey uncertainty with Fuzzy Grey Cognitive Maps
Expert Systems with Applications: An International Journal
Ranking fuzzy cognitive map based scenarios with TOPSIS
Expert Systems with Applications: An International Journal
Learning Fuzzy Grey Cognitive Maps using Nonlinear Hebbian-based approach
International Journal of Approximate Reasoning
Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps
IEEE Transactions on Software Engineering
A grey-based decision-making approach to the supplier selection problem
Mathematical and Computer Modelling: An International Journal
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Recently, Fuzzy Grey Cognitive Map (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it is focused on solving problems with high uncertainty, under discrete incomplete and small data sets. The FGCM nodes are variables, representing grey concepts. The relationships between nodes are represented by directed edges. An edge linking two nodes models the grey causal influence of the causal variable on the effect variable. Since FGCMs are hybrid methods mixing Grey Systems and Fuzzy Cognitive Maps, each cause is measured by its grey intensity. An improved construction process of FGCMs is presented in this study, proposing an intensity value to assign the vibration of the grey causal influence, thus to handle the trust of the causal influence on the effect variable initially prescribed by experts' suggestions. The explored methodology is implemented in a well-known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection, where the FCMs have previously proved their usefulness in decision support. Through the examined medical problem, the FGCMs demonstrate their functioning and dynamic capabilities to approximate better human decision making.