Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Type 2 representation and reasoning for CWW
Fuzzy Sets and Systems - Special issue: Approximate Reasoning in Words
Computing with words and its relationships with fuzzistics
Information Sciences: an International Journal
Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
Systematic design of a stable type-2 fuzzy logic controller
Applied Soft Computing
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
Interval type-2 fuzzy logic congestion control for video streaming across IP networks
IEEE Transactions on Fuzzy Systems
A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation
IEEE Transactions on Fuzzy Systems
An improved method for edge detection based on interval type-2 fuzzy logic
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Perceptual Reasoning for Perceptual Computing
IEEE Transactions on Fuzzy Systems
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The need of better representation of complex systems, such social systems, has made that the use of new simulation techniques are increasingly accepted, one of these accepted techniques are multi-agent systems. In addition to represent the uncertainty that is required by them, fuzzy logic and particularly type-2 fuzzy logic are being accepted. A system with three different types of agents is presented as case of study, each agent is assigned to a role with specific goals to be achieved in both ways individually and as teams, the success or failure is determined by group performance rather than individual achievement. It is also taken into account the environment or context as another type of agent. Fuzzy inference systems are defined for each of the agents to represent the concepts interpretation.