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• W1619865951 abstract "Systems containing uncertainty are traditionally analyzed with probabilistic methods. However, for nonlinear, non-Gaussian systems solutions can sometimes be very difficult to obtain. The focus of this research is to determine if in such cases fuzzy dynamic systems models may provide an alternative approach that more easily leads us to a good solution. In this article, we present a fuzzy estimator whose system model is a fuzzy dynamic system. We show that for the linear, Gaussian case the fuzzy estimator produces the same result as the Kalman filter." @default.
• W1619865951 created "2016-06-24" @default.
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• W1619865951 date "2002-12-24" @default.
• W1619865951 modified "2023-09-25" @default.
• W1619865951 title "Fuzzy dynamic model based state estimator" @default.
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• W1619865951 doi "https://doi.org/10.1109/isic.1996.556220" @default.
• W1619865951 hasPublicationYear "2002" @default.
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