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The Analytic Hierarchy Process (AHP) has received different fuzzy formulations, where two main lines of research can be identified in literature.
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The fuzzy formulation of the indices Cp and Cpk, most used two traditional PCIs, are produced when SLs are either triangular (TFN) or trapezoidal fuzzy numbers (TrFN).
This paper describes a systematic approach to the modeling of engineering systems using a fuzzy formulation that is independent of human knowledge.
It is shown how, given a fuzzy formulation of the variogram of a soil property, a fuzzy set of grid spacings that will achieve a target kriging variance can be derived.
Taking into account the financial factors and uncertainties in the product design process, this paper deals with a fuzzy formulation combined with a genetic-based interactive approach to QFD planning.
Zargar et al. (2015) included particle swarm optimization in fuzzy formulation and employed construction model for estimation of refractive index of crude oil.
Referring to Table 3, it can be observed that the fuzzy formulation led to lower costs for all cuts compared with the deterministic mode.
This work also proposes an extension of such fuzzy optimization formulation wherein the nature of trade-off between improving the product desirability and satisfying the fuzzy constraints are made explicit.
To mimic this diagnosis, the fuzzy linguistic formulation is used and a set of fuzzy rules are then established.
Instead of using complicated mathematical formulations, fuzzy logic uses human-understandable fuzzy sets and inference rules to obtain the solution that satisfies the desired system objectives.
As many linear LMI control results naturally generalize to such fuzzy systems, LMI formulations for fuzzy control became the tool of choice in the 1990s.
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