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The fuzzy modeling or fuzzy identification, first explored systematically by Takagi et al. [11].
The method combines a fuzzy identification methodology with some ideas from linear programming theory.
On-line adaptive fuzzy identification is introduced to identify the system parameters.
The method combines a fuzzy identification methodology with some ideas from applied statistics.
The ABFSM comprises an adaptive fuzzy identification controller (AFIC) and a B-spline-based compensation controller (BCC).
It is implemented based on a fuzzy identification algorithm called "Limited Rules", employed to model continuous nonlinear processes.
Similar(42)
The objective of this study is to develop a neuro-fuzzy identification model to predict the radial displacement of the arch dam.
To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared (FTIR) spectroscopy with the aid of a neuro-fuzzy identification model has been considered in this research.
This paper proposes a fuzzy-identification-based adaptive control scheme for the chaotic dynamic systems using backstepping control approach, which is referenced as adaptive fuzzy backstepping control (AFBC).
Fuzzy measure identification requires raw data to be turned into confidence degrees.
In this paper, an improved T-S fuzzy model identification approach is proposed.
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