Fuzzy Models of Dynamical Systems

  • János Abonyi


Model-based engineering tools require the availability of suitable dynamical models. Consequently, the development of a suitable nonlinear model is of paramount importance. Given the high expectations of fuzzy models in the area of identification and control, it becomes necessary to analyze and extract control-relevant information from fuzzy models of dynamical processes. Hence, in this chapter after an introduction to the data-driven modeling of dynamical systems, the following characteristics of TS fuzzy models are analyzed:
  • Fuzzy models of dynamical systems

  • State-space realization of the model

  • Prediction of the equilibrium points

  • Stability of the equilibrium points

  • Extraction of a linear dynamical model around an operating point Based on this analysis, new fuzzy model structures

  • Hybrid F\izzy Convolution Model

  • Fuzzy Hammerstein Model

are proposed; these models can more effectively represent special nonlinear dynamic processes than can conventional fuzzy systems.


Fuzzy Model Residence Time Distribution Hammerstein Model Linear Parameter Vary Wiener Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • János Abonyi
    • 1
  1. 1.Department of Process EngineeringUniversity of VeszprémVeszprémHungary

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