Sentence examples for rbf 1 from inspiring English sources

With RBF (1 bit CQI), the system can achieve a reduction in the feedback overhead of up to 7 bits out of total 10 bits when P = 10 dB in Figure 7.

Hybrid neural model training and testing results are exhibited in Appendix A. Table 1 Hour basis weather forecasting verification (2 days earlier) Neural network structure No. of days Correlation coefficient (R) RMSE Scatter index (SI) MLP 1 0.89 175 0.87 2 0.86 189 0.91 RBF 1 0.81 182 0.94 2 0.79 193 1.01 Hybrid neural model (MLP + RBF) 1 0.95 1590.691590.693 1590.699.

These genes encode for 15 RPLs, 12 RPSs, 8 90S RBFs, 7 60S RBFs, 1 40S RBF, 1 Exonuclease RBF, 1 Exosome RBF, 1 TRAMP complex RBF, and one not assigned RBF.

In SVM regression, radial basis function (RBF) (1) was the most commonly used kernel function for its better generalization ability, less number of parameters, and less numerical difficulties [ 33] and was used in this study.

Multilayer perception [26], RBF Network [30], IB1 [31], IBk [31], and J48 [34] shows good recognition performance among the tested classifiers at 5% training data.

Patients with RBF <0.04 nmol/L were significantly more likely to have severe hypoglycemia than patients with RBF >0.04 nmol/L (odds ratio, 2.59; 95% CI, 1.10 7.08; P < 0.03).

Table 3 The effect of different classifiers on detection performance Classifier D FP SVM RBF 93.0 6.6 Linear SVM 82.7 5.5 NN 93.4 10.4 KNN 84.9 5.6 LDA (linear) 91.2 10.6 LDA (quadratic) 88.9 13.9 The SVM with RBF kernel outperforms all other evaluated classifiers.

RVO2 and renal oxygen extraction (RO2Ex) were derived from the formulas RVO2 = RBF × (CaO2-CvO2) and RO2Ex = (CaO2-CvO2/CaO2), respectively, where CaO2 and CvO2 are the arterial and renal vein oxygen contents.

Table 2 Kernel parameters setting Regressive model Kernel C G D SVR rbf 250 0.01 3 SVR + HP rbf 275 0.1 3 SVR + CF rbf 150 0.01 3 SVR + BK rbf 250 0.1 3. The error rate is computed between the actual and predicted stock prices come from the experiments.

The RBF [39] is given as Kleft x,yright)= exp left(gamma x-{y}^2right) (17).

Table 5 Kernel parameters setting Regressive model Kernel C G D SVR rbf 175 0.01 3 SVR + HP rbf 275 0.01 3 SVR + CF rbf 250 0.1 3 SVR + BK rbf 200 0.01 3. Figure 9 shows the results of our regression by plotting the original data and regressive data together for different time series.