Although different mechanical and energetic optimum criteria have been proposed in the last decades by assuming involved parameters as deterministically known, nowadays the need persists to explore more realistic approaches for virtue of the unavoidable presence of uncertain variables.
In this section, the finite-time stability, robust finite-time stability, and robust stochastic finite-time stability will be analyzed by assuming the parameters of the filter to be designed are given.
The thickness of the elastic layer and the viscosity of the underlying layer are estimated by the grid search as unknown parameters optimization, so as to minimize the reduced χ2 value (e.g., Press et al., 1992) by assuming these parameters to be in the range between 17 and 35 km and 1.0 × 1017 and 9.0 × 1021 Pa s, respectively, based on previous studies (e.g., Suito and Hirahara, 1999).
In addition, we can propose how to modify this to a random effects model to incorporate the heterogeneity by assuming that the parameters underlying studies and the parameters of distribution of covariates follow some distribution.
Moreover, we circumvented mining the literature or using randomly generated values for thermodynamic parameters by assuming a single parameter for relating the forward and backward reaction rates to the overall rate for all reversible reactions.
By assuming that grammar parameters vary slowly, again along a phylogenetic tree, we can obtain substantial increases in grammar quality across the board.
By assuming the specific parameters of its wind farms and PV stations, and based on the historical data, we assume the city has one million EVs.
By assuming that these parameters do not depend on genomic context in most cases, we proposed our first methodology for the automatic design of genome rearrangements under changing environments.
Between-animal variation was modelled by assuming that the parameters for each animal are drawn from higher-order distributions, such that, b i (k ) ~ N (μ b i, σ b i 2 ).
The initial value of the model ( X d, C X d ) is obtained by assuming all parameter dispersion of a typical road during learning phase.
