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Tian et al. (2018) suggested a hybrid QFD-based (Quality Function Deployment) fuzzy MCDM approach based on a fuzzy maximizing deviation method (MDM) and the BWM approach along with the MULTIMOORA approach in Changsha, China in order to increases understand about a smart Bike-Sharing Program (BSP).
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Outliers were removed using the median absolute deviation method.
The approach developed as part of this particular study resembles k-means clustering, that is, minimizing the deviation (variance) within groups while maximizing the deviation (variance) between groups.
Residual-scaled local standard deviations method.
Furthermore, it was observed that by setting the AB parameter to 3, one is able to maximize the deviation away from the exponential distribution for the corticosteroid datasets, whereas an AB parameter of 4 maximized the deviation away from the exponential distribution for the circadian dataset and the burn datasets.
For maximizing such functions, various methods such as the steepest descent method, Newton's method, and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) [ 16] method have been developed.
This categorization method minimized each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups [9]. Figure 1 Regional maps of (a) mean annual NPP and (b) the standard deviation of mean annual NPP from 2000 to 2011 from the CASA model.
This categorization method minimized each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups [ 9].
There are actually several methods: maximizing a robustness criterion [6, Section 5.1.3], maximizing the detection score [1], or maximizing the error exponent [7].
Here we show analytically that maximizing rewards and minimizing deviations from the setpoint are equivalent objective functions.
They can be loaded with separate core and surface payloads, maximizing contrast for two imaging methods.
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