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The discretization uses a control volume methodology, with linear internodal variation of the flow variables.
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Here we present a novel parametric inference methodology for problems with linear parameter dependency that does not suffer from this drawback.
This novel element is formulated using the method of partial wave techniques (PWT) in conjunction with linear algebraic methodology.
The method combines a fuzzy identification methodology with some ideas from linear programming theory.
The paper develops a methodology whereby a Principal Agent model is combined with Linear Programming in order to deal with the design of environmental regulation in agriculture.
However, in order to test whether the nonlinearity assumption is true and to evaluate the performance of the proposed ANN methodology, in this section, we compare the performance of the proposed methodology with the ones of a linear model.
The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost.
At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used.
Case studies with linear and nonlinear models of an evaporator illustrate the methodology and limitations of linearity assumptions.
This paper develops an interval observer design methodology for linear parameter varying (LPV) systems with parametric uncertainty.
In the specific case of the 2D-FPT methodology for ACHE and DHFR datasets, the achieved results are based on non-linear models while the proposed outcomes are determined with linear models.
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