2018Engineering Mechanics ...Open access

Uncertainty quantification through a model-based fuzzy set membership function

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Abstract

An approach to uncertainty quantification is proposed.It is based on a fuzzy set membership function that is defined through a model (or models) response.That is, information about uncertain parameters is gathered from measured responses of one or more models, the membership function is inferred and used in uncertainty quantification of the output of a model of interest.The idea is illustrated by a beam deflection problem.

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An approach to uncertainty quantification is proposed.It is based on a fuzzy set membership function that is defined through a model (or models) response.That is, information about uncertain parameters is gathered from measured responses of one or more models, the membership function is inferred and used in uncertainty quantification of the output of a model of interest.The idea is illustrated by a beam deflection problem.

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Available abstract

An approach to uncertainty quantification is proposed.It is based on a fuzzy set membership function that is defined through a model (or models) response.That is, information about uncertain parameters is gathered from measured responses of one or more models, the membership function is inferred and used in uncertainty quantification of the output of a model of interest.The idea is illustrated by a beam deflection problem.

Key concepts: Membership function, Fuzzy set, Fuzzy logic, Uncertainty quantification, Function (biology), Computer science, Uncertainty analysis, Data mining

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