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A fuzzy procedure is designed for quasi-optimal network structure determination based on learning stage information.
All the rules created during the fuzzy procedure are considered to establish the model between descriptor hyperspace and biochemical activities.
Salmeri et al. [29] introduced different weights to various subregions based on a similarity measure followed by a fuzzy procedure to estimate the variance of noise.
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Wu and Tsai (2000) developed weighted-based fuzzy clustering procedure to estimate time-to-failure distribution.
Second, a fuzzy evaluation procedure is developed for finding reference designs with similar technological specifications.
In the proposed methodology the criteria weights are generated by a fuzzy AHP procedure.
A novel adaptive fuzzy control procedure for the temperature of MCFC stack is also developed.
Moreover fuzzy programming procedure has been developed to optimize of the proposed closed loop supply chain network design model.
The Fuzzy AHP procedure presented by Mikhailov (2003) was then utilized to weight the screened set of criteria.
The presented fuzzy modeling procedure is used to describe two signal transduction pathways, one for IL-6 and one for TNF-α signaling.
The value of each of these indexes is finally obtained as a result of a fuzzy inference procedure, which is a crisp value ranging from 0 to 1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com