## 4 Results and Discussion  
Using both tools, we were allowed to predict new values based on the best models. Since the training set was created by combining values from different materials, it verified (Table 1) its suitability by comparing it with the measured data for individual materials separately. MS Excel was used to calculate the values. Sample calculation (4) for the first record for material VO_20_PVB_80_TF-KS1. \[R_{1}^{2}=1-\frac{\left(y_{1}-\hat{y}_{1}\right)^{2}}{\left(y_{1}-\overline{y} _{1}\right)^{2}}=1-\frac{\left(0.578153-0.562876\right)^{2}}{\left(0.578153-0.2 91378\right)^{2}}=1-\frac{0.000233}{0.08224}=0.997166 \tag{4}\]  
Based on the results of individual qualitative indicators, it can be seen that the model obtained through neural net fitting achieves better results for both test samples, for both states (KS1 and KS2). This is because the model design environment offers more flexibility to influence individual settings. The \"overtraining\" option also increases the value of the R coefficient by making the model \"learn\" more. In the RL environment, it is also possible to influence individual coefficients, but only after generating the function and only by manual change. Variations between test samples may be due to the fact that the created models were only applied to the actual sample size--that is, to 1869 values. Available data (Figure 9) pairing, validation, and selection procedures are involved in implementing the prediction management model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \multicolumn{4}{c}{**Neural Net Fitting**} & \multicolumn{4}{c}{**Regression Learner**} \\ \hline Material & VO-20-PVB-80-TF & VO-50-PVB-50-TF & VO-20-PVB-80-TF & VO-50-PVB-50-TF \\ State & KS1 & KS2 & KS1 & KS2 & KS1 & KS2 & KS1 & KS2 \\ \(R^{2}\) & 0.9052 & 0.9866 & 0.7302 & 0.8655 & 0.7617 & 0.6325 & 0.6448 & 0.5659 \\ MAPE & 8.56\% & 11.21\% & 12.04\% & 15.37\% & 14.92\% & 27.11\% & 18.64\% & 29.45\% \\ MSE & 0.00094 & 0.000708 & 0.00359 & 0.00209 & 0.00237 & 0.00432 & 0.00473 & 0.00188 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of proposed models [authors’ own processing]. Figure 8: Regression learner environment—a prediction model [authors own processing]. The model designed using the neural net fitting tool achieves better results of qualitative indicators. We will apply this model to obtain the assumed values for the material VO_30_PVB_70_TF. Application of Selected Model for VO_30_PVB_70_TFThis material was not subjected to tests and measurements in a climatic chamber. We will use the selected model to predict these values. From the original set of values, the essential column will be \"Absorbance\", which forms the basic input for both models. The following Table 2 shows some of the source values. By importing the values into the Matlab environment and subsequent modification, we will use the generated functions of the selected model and get the expected KS1 and KS2 values for the assessed material (Figure 10). The next figure shows a graphical interpretation of gained data using the selected model. The values of \"Absorbance\" are measured and values of \"KS1\" and\"KS2\" are predicted. The next Table 3 shows part of the gained values KS1 and KS2 for the observed material. Experiments in the climate chamber require careful planning, take a certain amount of time, and are also financially demanding. The goal of this paper was to predict the absorption values for the material VO_30_70_PVB_TF, which was not subjected to the test in the climate chamber. The authors consider the creation of a model to be a benefit, which will enable obtaining values of the impact of possible climate changes on a material with a given structure, which was not tested in a climate chamber. By using the Matlab tool, we can obtain both the graphical form and numerical values of the observed property.