This method uses a semidefinite relaxation, while enforcing stability on the reduced order model for all values of parameters of interest.
Additionally, we ran an equal effects model for all values of P. The mutation rate and average effect also were estimated using the Bateman Mukai approach (Bateman 1959; Mukai 1964).
To access the impact of various parameters on model outcomes and to confirm the robustness of the model, all parameter values were varied individually in one-way sensitivity analysis (SA) and simultaneously in a probabilistic SA with 10.000 iterations.
By normalizing the membership matrix elements based on fuzzy comprehensive evaluation model, all the values of the matrix elements are restrained in the range of 0.0 to 1.0, hence the complexity of the calculations is reduced.
Specifically, in the sin model, all predicted values but 3 outliers fell within the standard errors of the mean observed pleasantness of the mixtures at their various concentrations.
From these results, it can be concluded that the precision achieved with the proposed HDLA-based image representation is obviously better than the existing models across all values of recall for all image collections selected for evaluation.
It is clearly observed from Figure 8 for tolerance levels 0.05 and 0.1 HME3M is outperforming all comparison models for all values of M. However for tolerance 0 we initially observe the polynomial and radial SVM kernels outperforming both HME3M and linear SVM.
