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In the current methods for establishing the failure prognosis model, the qualitative knowledge and quantitative information (life data and monitoring observation) cannot be used effectively and simultaneously.
By contrast, a safety model using fuzzy logic approach employing fuzzy IF THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses.
Our general approach is instantiated precisely on three models: the probabilistic expected utility model, the qualitative pessimistic minmax model and the concordance rule, which are all constructed from a weight vector.
By contrast, a fuzzy logic approach employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses.
Besides these findings, the proposed model can produce more reasonable and understandable rules, because the "if-then" rules produced by RST can model the qualitative aspects of human knowledge.
Accordingly, modeling using a RST to produce rules can overcome the limitations of statistical methods and the produced "if-then" rules can model the qualitative aspects of human knowledge applicable for decision makers.
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In particular, drawing analogies from the phase plane analysis of single cell models, our model explains the qualitative transitions in terms of what can be regarded as population excitability.
Furthermore, the excessive nonlinearity introduced additional sensitivity to the models: the qualitative (Table 1) and quantitative (Figure 7) comparisons suggest that nonlinearity also increases the sensitivity of these complex models, without major perturbation to the dynamics of the model.
Moreover, initial results presented here suggest that while the quantitative values of sensitivity coefficients calculated using different models with overlapping biology will change between models, the qualitative conclusions drawn may be invariant.
This simplified version of the Atri model captures the qualitative features that are important to our discussion, but has a much simpler functional form than the full model, making it easier to work with.
Our model captures the qualitative noise features of both experiments and accurately fit the data from the first experiment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com