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Usually, these generators are based on numerical algorithms that produce seemingly unpredictable number sequences.
The simulation of these type of systems is based on numerical algorithms, whereby the solution of a system of equations, i.e., the semantics of a Simulink model, is given by the sequence of values that represents the temporal functions [11].
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Parameter estimation was carried out by using computer programmes based on numerical calculation algorithms.
Then, a data acquisition algorithm based on numerical analysis and Lagrange interpolation is proposed to acquire the critical points.
Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point.
The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure.
Two critical point aware data acquisition algorithms are proposed based on numerical analysis [27] and Lagrange interpolation [28] techniques.
Then, a data acquisition algorithm is proposed based on numerical analysis and Lagrange interpolation.
In this paper, a novel sensory data acquisition algorithm is proposed based on numerical analysis techniques [27] and Lagrange interpolation [28] in order to retrieve the critical points approximately.
Four regression equations were analyzed and two techniques of adjustment were applied: the least-squares of the residuals in the logarithmic domain, including their bias correction, and another based on numerical optimization, using the multivariable unconstrained Rosenbrock algorithm.
Being based on reliable numerical algorithms such as the singular value decomposition (SVD), subspace methods do not require nonlinear optimization and, thereby, are computationally efficient and stable without suffering from convergence problems.
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