International Journal of Man-Machine Studies
Hidden patterns in combined and adaptive knowledge networks
International Journal of Approximate Reasoning
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural network PC tools: a practical guide
Neural network PC tools: a practical guide
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Fuzzy cognitive maps considering time relationships
International Journal of Human-Computer Studies
Cognitive mapping and certainty neuron fuzzy cognitive maps
Information Sciences: an International Journal
Fuzzy cognitive maps: a model for intelligent supervisory control systems
Computers in Industry - ASI 1997
Networks and Chaos - Statistical and Probabilistic Aspects
Networks and Chaos - Statistical and Probabilistic Aspects
Reasoning and unsupervised learning in a fuzzy cognitive map
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences: an International Journal
A high performance edge detector based on fuzzy inference rules
Information Sciences: an International Journal
Group decision making based on multiple types of linguistic preference relations
Information Sciences: an International Journal
A method for group decision making with multi-granularity linguistic assessment information
Information Sciences: an International Journal
Genetic learning of fuzzy cognitive maps
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cognitive reasoning using fuzzy neural nets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy cognitive map approach for effect-based operations: An illustrative case
Information Sciences: an International Journal
Adaptive estimation of fuzzy cognitive maps with proven stability and parameter convergence
IEEE Transactions on Fuzzy Systems
Transformation of cognitive maps
IEEE Transactions on Fuzzy Systems
Agent based mobile negotiation for personalized pricing of last minute theatre tickets
Expert Systems with Applications: An International Journal
Learning fuzzy cognitive maps from data by ant colony optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Intelligent Decision Technologies
A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning
Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications
A fuzzy cognitive map of the psychosocial determinants of obesity
Applied Soft Computing
Bi-linear adaptive estimation of Fuzzy Cognitive Networks
Applied Soft Computing
Dynamic green self-configuration of 3G base stations using fuzzy cognitive maps
Computer Networks: The International Journal of Computer and Telecommunications Networking
Expert Systems with Applications: An International Journal
Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points
Applied Soft Computing
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In this paper, we compare the inference capabilities of three different types of fuzzy cognitive maps (FCMs). A fuzzy cognitive map is a recurrent artificial neural network that creates models as collections of concepts/neurons and the various causal relations that exist between these concepts/neurons. In the paper, a variety of industry/engineering FCM applications is presented. The three different types of FCMs that we study and compare are the binary, the trivalent and the sigmoid FCM, each of them using the corresponding transfer function for their neurons/concepts. Predictions are made by viewing dynamically the consequences of the various imposed scenarios. The prediction making capabilities are examined and presented. Conclusions are drawn concerning the use of the three types of FCMs for making predictions. Guidance is given, in order FCM users to choose the most suitable type of FCM, according to (a) the nature of the problem, (b) the required representation capabilities of the problem and (c) the level of inference required by the case.