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GA-MLR

Hemmateenejad B, Safarpour MA, Miri R, Taghavi F. Application of ab initio theory to QSAR study of 1,4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures. I Comput Chem 2004 25 1495-503. [Pg.387]

Liu, S., Yin, C.-S. and Wang, L.-S. (2002b) Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides, and COX-2 inhibitors. [Pg.1107]

Table 2. RMSCV, RMSEP and the numbers of selected variables for modelling yl, y2, y3, y4 and y5 by GA-MLR. Table 2. RMSCV, RMSEP and the numbers of selected variables for modelling yl, y2, y3, y4 and y5 by GA-MLR.
Fig. 6 The y,versus y h.scrmi plots ( for test set) according to the GA-MLR models. Fig. 6 The y,versus y h.scrmi plots ( for test set) according to the GA-MLR models.
Only for yl the predictive ability of the GA-MLR model is not satisfactory. For y2, y3, y4 and y5, the prediction of the models are excellent, but as one can notice, few of the selected variables are on the data baseline, which suggests that the models can prove unstable. In fact, this is a case with these models. If to the independent test set a small noise is added (simulated as randn(mt,nt) 0.001), the constructed models failed completely. Plots of ypredkted versus yobserved for the noisy test set, denoted as Xtn, are presented in Fig. 7. [Pg.338]

Results of PLS, i.e. of the full spectrum method, are presented in Table 3. The RMSCV and RMSEP values are much higher than the analogous values observed for the SMLR or GA-MLR models, but one can hope that the PLS models are more stable e.g. when instrumental problems occur. Still, one can try to lower model complexity by extracting relevant information from the original spectra. This can be done, for instance, by using the UVE-PLS or... [Pg.338]

Table 5. RMSEP for the test set contamined with white noise (Xtn) for the GA-MLR, PLS, UVE-PLS and RCE-PLS models. Table 5. RMSEP for the test set contamined with white noise (Xtn) for the GA-MLR, PLS, UVE-PLS and RCE-PLS models.
MEDV-GA-MLR Method for QSAR of Three Panels of Steroids, Dipeptides and COX-2 Inhibitors. [Pg.347]

GA-MLRs Genetic algorithm-multiple linear regressions... [Pg.924]

Kohonen network Conceptual clustering Principal Component Analysis (PCA) Decision trees Partial Least Squares (PLS) Multiple Linear Regression (MLR) Counter-propagation networks Back-propagation networks Genetic algorithms (GA)... [Pg.442]

Boger et al. [50] analyzed the performance of MLRs with internal density-driven circulation (IMLR). They found the gas-hquid mass transfer superior and the overall mass transfer performance comparable with those for slurry reactors. [Pg.196]

To avoid over-fitting, a commonly used approach is to select a subset of descriptors to build models. GAs are widely used to select descriptors prior to using other statistical tools, such as MLR, to build models. Certainly, principal component analysis and PLS fitting are also widely used in reducing the dimensions of descriptors. Traditionally, stepwise linear regression is used to select certain descriptors to enter the regression equations. [Pg.120]

The RHR plots for PP-MAPP-Cloisite 20A nanocomposite and PP at 35 kW/m heat flux shown in Figure indicate a 60% - decrease of peak of RHR (Fig. 11). Comparison of the Cone calorimeter data PP and PP-MAPP- 7% Cloisite 20A reveals that the specific heat of combustion (He), specific extinction area (SEA), a measure of smoke yield, and carbon monoxide yields are practically unchanged this suggests that the source of the improved flammability properties of these materials is due to differences in condensed-phase decomposition processes and not to a gas-phase effect. The primary parameter responsible for the lower RHR of the nanocomposites is the mass loss rate (MLR) during combustion, which is significantly reduced from the value observed for the pure PP (Fig. 12). It is supposed, that this effect is caused by ability to initiate the formation of char barrier on a surface of burning polymeric nanocomposites that drastically limits the heat and mass transfer in a burning zone. [Pg.56]


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See also in sourсe #XX -- [ Pg.465 ]




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