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Based on the redistribution of fuzzy errors from the Markov transition technique, the fuzzified form of the forecast error ( mu (e_{n + 1},m) ) is then obtained as mu (e_{n + 1},m) = Pmu (e_{k},m) (9) 2.
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This paper considers the design of fuzzy error governors for multivariable systems subject to input saturation.
Improvements are made to an existing fuzzy error governor, which expands the class of systems to which the technique can be applied.
The goal of this paper is to handle the large variation issues in fuzzy data by constructing a variable spread multivariate adaptive regression splines (MARS) fuzzy regression model with crisp parameters estimation and fuzzy error terms.
Probabilistic record linkage or other "fuzzy", error-tolerant matching techniques would be used for this matching or "lookup" operation, possibly assisted by human intervention where required.
The modes and spreads of the fuzzy random errors Φ t are chosen as random samples from normal and uniform distributions, N ( 0, 4 ), U [ 0, 5 ], respectively.
The modes and spreads of fuzzy random errors Φ t are chosen as random samples from normal and uniform distributions, N ( 0, 1 ), U [ 0, 0.5 ], respectively.
Additionally, we derive a suitable parameter adaptation law so that the proposed observer is robust with respect to ubiquitous fuzzy approximation errors and external disturbances.
(2) Given the prescribed disturbance attenuation level ρ. (3) Represent the nonlinear stochastic synchronization error dynamic by the T-S fuzzy synchronization error dynamic model, using the interpolation of several local linear stochastic systems.
To estimate the output of positive T S fuzzy systems, error-bounding positive filters are constructed.
An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances.
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