Sentence examples for approximation of the response from inspiring English sources

This paper proposes an iterative RSM framework, where Gaussian process (GP) regression models are applied for the approximation of the response surface.

By the new equivalent forms of the variance-based sensitivity indices and the multiplication approximation of the response function, the proposed method can simultaneously estimate all the order effects by repeatedly making use of the same sample points.

In order to improve the efficiency, the accuracy and the robustness of the sampling-based methods for estimating the variance-based sensitivity indices, a new efficient method based on the combination of the unconditional expectation, the conditional expectation and the multiplication approximation of the response function is proposed in this paper.

In the analysis procedure, the approximation of the response, y, was determined using a quadratic polynomial regression model as a function of the pyrolysis parameters using Eq. 1, which has linear and quadratic terms in addition to an interaction term, where b is the regression coefficient, χ is the independent parameter, and e is the experimental error.

This is a first approximation of the response of the underlying layers, based on the assumption of a complete randomization of the polarization and emerging direction of the backscattered photons due to a large number of scattering events.

At first sight the accuracy of the PCE approximation of the response surface - and thus of the statistics - seems to be determined by the number of expansion terms, N, in Eq. (3).

This difference may be due to the weak approximation on the response of the vibro-impact oscillators using a continuous Markov process when the restitution coefficient is not very close to unity.

Results show that finite element approach with orthogonal function approximation of the responses give more accurate results, in general, than the exact solution approach for all the studies presented in this paper.

The fitness function in the guide-based GA is evaluated using convex approximations of the response.

High quality approximations of the response functions are introduced and used to evaluate both the constraint values and constraint derivatives.

The classical theory has been demonstrated to provide a good approximation to the response of real materials down to small length scales, particularly in single crystals, provided these assumptions are met.