International Journal of Man-Machine Studies
Introduction to Grey system theory
The Journal of Grey System
Fuzzy engineering
Disconcepts and Fuzzy Cognitive Maps
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
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
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
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
A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning
Knowledge-Based Systems
Fuzzy Grey Cognitive Maps in reliability engineering
Applied Soft Computing
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Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it has become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. The proposed approach of learning FGCMs applies the Nonlinear Hebbian based algorithm determine the success of radiation therapy process estimating the final dose delivered to the target volume. The scope of this research is to explore an alternative decision support method using the main aspects of fuzzy logic and grey systems to cope with the uncertainty inherent in medical domain and physicians uncertainty to describe numerically the influences among concepts in medical domain. The Supervisor-FGCM, trained by NHL algorithm adapted in FGCMs, determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Three clinical case studies were used to test the proposed methodology with meaningful and promising results and prove the efficiency of the NHL algorithm for FGCM approach.