Discovering the dynamic behavior of unknown systems using fuzzy logic

• Published in: Fuzzy optimization and decision making 2018-12, Vol.17 (4), p.421-445
• Main Authors: Javier Barragán, A., Enrique, Juan M., Calderón, Antonio J., Andújar, José M.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Economic models; Fuzzy logic; Fuzzy systems; Mathematical Logic and Foundations; Mathematical models; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Probability Theory and Stochastic Processes; Systems analysis
• ISSN: 1568-4539; 1573-2908
• DOI: 10.1007/s10700-018-9285-4

To know the dynamic behavior of a system it is convenient to have a good dynamic model of it. However, in many cases it is not possible either because of its complexity or because of the lack of knowledge of the laws involved in its operation. In these cases, obtaining models from input–output data is shown as a highly effective technique. Specifically, intelligent modeling techniques have become important in recent years in this field. Among these techniques, fuzzy logic is especially interesting because it allows to incorporate to the model the knowledge that is possessed of the system, besides offering a more interpretable model than other techniques. A fuzzy model is, formally speaking, a mathematical model. Therefore, this model can be used to analyze the original system using known systems analysis techniques. In this paper a methodology for extract information from unknown systems using fuzzy logic is presented. More precisely, it is presented the exact linearization of a Takagi–Sugeno fuzzy model with no restrictions in use or distribution of its membership functions, as well as obtaining its equilibrium states, the study of its local behavior and the search for periodic orbits by the application of Poincaré.