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The interpolation weights can be obtained by solving this linear system of equations.
By solving this linear problem, the total value of the objective function will be defined and attributed to that chromosome.
The coefficients (c_{j}^{n}) in the approximate solution (20) can be determined by solving this linear system.
The values of Oil variables in the h-th layer obtained by solving this linear equations are utilized as that of Vinegar variables in the (h+1 -th layer.
end{cases} By solving this linear system and using the inverse Laplace transform, we get begin{cases} underline{y} x,alpha)= (alpha-1 cosh(x)+(1-2alpha)sinh alpha-1 coshrac{x^{2}}{2}-2x -3, overline{y}(x,alpha)= 2(1-alpha)cosh(x)+ 1-2alpha sinh(x)+2e^{x}-frac{x^{2}}{2}-2x -3.
The above equation has a general form A x = b, then x = [ η 1 Ω,..., η N F Ω ] T can be computed by solving this linear system of equations.
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By solving this non-linear programming (NLP) model, whose objective is to minimize the annual cost, an optimal cogeneration system can be obtained.
By solving the linear system (35 - 36 35 - 36t.
Then, by solving the linear matrix inequalities (LMIs), the filter parameter matrices can be obtained.
By solving the linear system of Eq. (22), the coefficient matrix U can be found.
By solving the linear system (5), one determines λ and c, and hence s(x).
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