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The proposed non-linear mixed integer programming model was linearized using auxiliary variables.
The model was linearized and solved via a geospatial regression model to deal with spatial dependency in data.
The process model was linearized in three functioning points (rain, normal and drought) and a robust controller, using QFT method, has been designed.
To find out the optimal solution in a reasonable time, the proposed nonlinear model was linearized using auxiliary variable.
To determine the values of K and n, the Korsmeyer-and-Peppas model was linearized: (2) M t M ∞ = K t n, (3) ln M t M ∞ = ln K + ln t n.
The first-order kinetic model was linearized as shown in (2) [ 28]: (1) − d M d t = k M, (2) ln (M u M u − M t ) = k t, where t (day) is time and M u and M t (L ·kg−1CH) are methane yields obtained in 55 days and t days, respectively, and k is the specific rate constant.
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The original non-linear mathematical model is linearized using a set of piecewise linear secant approximations.
The original non-linear model is linearized and then solved using CPLEX.
The model is linearized and several linear controllers are designed for actuation at certain situations, in a way that the fuzzy control allows to handle these controllers globally.
The model is linearized and several linear controls are designed for their actuation at certain situations, in a way that the fuzzy control allows to handle those controls globally.
First, the helicopter model is linearized with approximate feedback linearization technique.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com