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Modeling and predicting the response and failure of laminated composite structures is a very challenging task.
The optimization involves estimation of the coefficients in a mathematical model and predicting the response and checking the adequacy of the model.
A second-order polynomial model was applied for fitting experimental data and predicting the response, and this mathematical model produced a satisfactory fit (R2 = 0.993, p ˂ 0.01).
The cylinder pressure force is expanded in a Fourier series for evaluating the bearing characteristics, establishing the balancing and predicting the response of the entire compressor.
RSM consists of three major steps: performing statistically designed experiments, estimating the coefficients in a mathematical model and predicting the response and checking the adequacy of the model (Bhaumik et al. 2013).
These linearcombinations of X-variables give PLS components (score vectors t).A PLS component can be thought of as a new variable - a latent variable - reflectingthe information in the original X-variables that is of relevance formodeling and predicting the response Y-variable by means of the maximizationof the square of covariance (Max cov2 X, Y)).
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Principally, this optimization process follows three major steps viz., performing the statistically designed experiments, estimating the coefficients in a mathematical model and predicting the responses and examining the adequacy of the model (Myers and Montgomery 1995; Sadhu et al. 2014).
Moreover, it is conducive to acquire the characteristics and predict the response of the system.
Such models may guide and predict the response to therapy in many areas of medicine.
This work compares and predicts the response of roof edge components to wind load.
The network is modeled using the two-port theory that splits the network into several cascaded elements, and predicts the response of the network.
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