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A new approach for bootstrap testing fuzzy hypotheses based on fuzzy test statistic is introduced.
The creation of a specific-pathogen-free mouse by Nelson and Collins gave the Dubos group a new and reliable standardized "fuzzy test tube".
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The performance is also compared to other fuzzy classifiers tested on the same databases.
With normal approximation to the distribution of the estimated SPK, two useful criteria for fuzzy hypothesis testing, critical value and fuzzy p-value, are developed to assess process yield based on SPK.
It is a good idea to test fuzzy features, to test old bugs, and especially, to retest persistent problems.
To assess performance of constructed fuzzy model, unseen test data were input into it and formation volume factor was computed.
Then two-stage fuzzy synthetic evaluation tests of die-face design are conducted.
Besides, a questionnaire application including these factors has been applied to determine the values of fuzzy training and testing sets.
These databases have been used to train and test fuzzy-logic based neural-network models for predicting the mechanical properties.
They were then submitted to fuzzy c-means, testing cluster validity with both internal and external criterion measures.
The fuzzy controller was tested for compensating step-change perturbations of propionic acidic flow rates, propionic acid concentration, and buffering conditions.
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