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The structure proposed in equation (6) was tested and validated by comparing the outcomes of two different modeling schemes, namely linear time series modeling and fuzzy modeling.
The two analytical tools used in this study to forecast daily maximum ozone levels are time series modeling and fuzzy modeling.
In this paper non-probabilistic uncertainty modeling is discussed by means of interval modeling and fuzzy methods.
Intelligent LOPA with modeling and fuzzy logic have been covered.
He discussed and evaluated some of the advanced computational modeling approaches; in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling.
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The QNN identifier, a computational tool for fuzzy classification combining the advantages of neural modeling and fuzzy-theoretic principles, has the ability to autonomously detect the presence of uncertainty, adaptively learn the existing uncertainty, properly approximate any membership profile, and autonomously quantify uncertainty in sample data.
The proposed control scheme includes a fuzzy tracking controller with state feedback plus the tracking error integral, a robust nonlinear compensator for eliminating the matched part of the compound disturbance consisting of the unmodeled disturbance and fuzzy modeling error, and a DO-based compensator for the modeled disturbance cancellation.
Fuzzy relational equations have been intensively investigated as an important tool for fuzzy modeling and approximate reasoning (see, e.g., [1, 2]).
A systematic design methodology for integrating fuzzy modeling and adaptive control is proposed and developed in this paper.
The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory.
Table 4 shows the other modeling inputs, implying that a variety of uncertainties presented as different formats (e.g. deterministic and fuzzy intervals) exist in the modeling parameters.
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