International Journal of Man-Machine Studies
Is There a Future for AI Without Representation?
Minds and Machines
An ontology for causal relationships between news and financial instruments
Expert Systems with Applications: An International Journal
Knowledge formalization in experience feedback processes: An ontology-based approach
Computers in Industry
Experimenting statecharts for multiple experts knowledge elicitation in agriculture
Expert Systems with Applications: An International Journal
Augmented fuzzy cognitive maps for modelling LMS critical success factors
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Conceptual modeling of causal map: Object oriented causal map
Expert Systems with Applications: An International Journal
Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications
Towards an Understanding of Hierarchical Architectures
IEEE Transactions on Autonomous Mental Development
Modeling complex systems using fuzzy cognitive maps
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On causal inference in fuzzy cognitive maps
IEEE Transactions on Fuzzy Systems
Dynamical cognitive network - an extension of fuzzy cognitive map
IEEE Transactions on Fuzzy Systems
Learning Algorithms for Fuzzy Cognitive Maps—A Review Study
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps
Applied Intelligence
Modeling maintenance projects risk effects on ERP performance
Computer Standards & Interfaces
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This work develops an intelligent tool based on fuzzy cognitive maps to supervisory process control. Fuzzy cognitive maps are a neuro-fuzzy methodology that can accurate model complexly system using a causal-effect fuzzy reasoning. In the proposed approach, new types of concept and relation, not restricted to cause-effect ones, are added to the model resulting in a dynamic fuzzy cognitive map (D-FCM). In this sense, a supervisory system is developed in order to control a fermentation process. This process has a non-linear behavior and presents several problems, such as non-minimum phase and large accommodation time. The supervisor goal is to operate the process in normal and critical conditions. The expert knowledge about the process behavior in both conditions is used to build the D-FCM supervisor. Simulation results are presented in order to validate the proposed intelligent supervisor.