Sentence examples for maximum response models from inspiring English sources

According to maximum response models, maximum shrub development was also limited by overstory variables in P. pinaster and P. radiata stands.

CHAID and quantile regression procedures were used for obtaining mean and maximum response models, respectively, of shrub characteristics (cover, height and fine fuel load).

Two key parameters were estimated from this model - Emax which is an estimate of the maximum response predicted by the model given the observed trough FEV1 and ED50 which represents the dose that achieves 50% of Emax.

The yield force F y can be found from the ductility and maximum response force using hysteretic-model-specific equations.

In this study, response surface methodology (RS M and artificial neural network (ANN) modeling were compared in terms of their estimation capabilities for building effective models with maximum response values.

We applied the heterogeneous choice model procedure, 84 for which we fitted heteroscedastic binary response models via maximum likelihood and ran likelihood tests to compare both models (results can be requested from the author).

The experimental results show that the maximum response of the bridge deck model was reduced by 94% when a SS-PTMD with mass ratio of 2% was applied.

Under the optimal conditions (13.79 min extraction time, 33.82 % amplitude and 30.99 % ethanol proportion), DOE and ANN models predicted a maximum response of 140.55 and 138.3452 mgGAE/gdw, respectively.

Nevertheless, the benefits derived from decomposing the maximum response rate in the progressive-ratio model suggests that a similar manoeuvre may be in order in the case of Herrnstein's (1970) equation (Bradshaw, 1994).

An important feature of the model is the decomposition of maximum response rate, allowing post-reinforcement pausing to be treated separately from purely motor constraints on responding.

The tuning bandwidths, maximum response magnitudes and baseline spontaneous firing magnitudes of the model AI neurons were independently and randomly drawn from the corresponding distributions.