Abstract
A Fuzzy Cognitive Map (FCM) is a powerful technique for modeling and analyzing complex systems. In this study, we propose a novel learning algorithm that, unlike existing FCM-based learning algorithms, ensures matching the desired system state by computing the otherwise “unexplained” biases in the model. Our learning algorithm considers both the whole system bias and the individual biases for each system factor (concept). We explore the impact of FCM structure and characteristics for the proposed algorithm and suggest an interpretation of computed biases. Finally, we propose an FCM visualization technique which enables comparison between and a deeper understanding of modeled systems. As FCMs offer a broader, quantifiable view of the causal relationships between factors, the approach used in this study provides insights into FCM modeling and application to real-world complex systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
