## 3 Prediction Hybrid Method
### Hybrid Method Testing
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model No.**} & \multirow{2}{*}{**Method Type** **(acc. to Table 4)**} & \multirow{2}{*}{**Input (acc. to Table 1)**} & \multirow{2}{*}{**Output (acc. to Table 2)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**R**} & **The Correctness of the Classification of the All Technical Conditions** & **The Correctness of the Second Condition \(S_{2}\)** \\ \hline \multirow{2}{*}{1} & (1) ANN & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.12497} & \multirow{2}{*}{0.75943} & \multirow{2}{*}{59.8} & \multirow{2}{*}{4.3} \\  & F-T-Lm & & & & & \\ \hline \multirow{2}{*}{2} & (1) ANN & \multirow{2}{*}{2} & \multirow{2}{*}{1} & \multirow{2}{*}{0.13384} & \multirow{2}{*}{0.67901} & \multirow{2}{*}{41.2} & \multirow{2}{*}{31.9} \\  & F-T-Lm & & & & & & \\ \hline \multirow{2}{*}{3} & (1) ANN & \multirow{2}{*}{2} & \multirow{2}{*}{1} & \multirow{2}{*}{0.11970} & \multirow{2}{*}{0.71918} & \multirow{2}{*}{48.1} & \multirow{2}{*}{10.6} \\  & F-T-Lm & & & & & & \\ \hline \multirow{2}{*}{4} & (2) ANN & \multirow{2}{*}{2} & \multirow{2}{*}{1} & \multirow{2}{*}{0.11913} & \multirow{2}{*}{0.72838} & \multirow{2}{*}{53.2} & \multirow{2}{*}{17.0} \\  & F-TP-Lm & & & & & & \\ \hline \multirow{2}{*}{5} & (2) ANN & \multirow{2}{*}{2} & \multirow{2}{*}{1} & \multirow{2}{*}{0.11669} & \multirow{2}{*}{0.71501} & \multirow{2}{*}{49.3} & \multirow{2}{*}{12.8} \\  & F-TP-Lm & & & & & & \\ \hline \multirow{2}{*}{6} & (2) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{0.069108} & \multirow{2}{*}{0.84538} & \multirow{2}{*}{76.5} & \multirow{2}{*}{38.3} \\  & F-TP-Lm & & & & & & \\ \hline \multirow{2}{*}{7} & (3) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{0.043195} & \multirow{2}{*}{0.87944} & \multirow{2}{*}{76.2} & \multirow{2}{*}{38.3} \\  & F-TP-Br & & & & & & \\ \hline \multirow{2}{*}{8} & (4) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{0.038862} & \multirow{2}{*}{0.88206} & \multirow{2}{*}{78.1} & \multirow{2}{*}{42.6} \\  & F-TP-Lm & & & & & & \\ \hline \multirow{2}{*}{9} & (5) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{0.014731} & \multirow{2}{*}{0.9222} & \multirow{2}{*}{78.6} & \multirow{2}{*}{61.7} \\  & F-T-Br & & & & & & \\ \hline \multirow{2}{*}{10} & (5) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{0.020364} & \multirow{2}{*}{0.92088} & \multirow{2}{*}{82.0} & \multirow{2}{*}{61.7} \\  & F-T-Br & & & & & & \\ \hline \multirow{2}{*}{11} & (6) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{2} & \multirow{2}{*}{0.064105} & \multirow{2}{*}{0.8938} & \multirow{2}{*}{81.5} & \multirow{2}{*}{80.9} \\  & F-T-C & & & & & & \\ \hline \multirow{2}{*}{12} & (2) ANN & \multirow{2}{*}{3} & \multirow{2}{*}{2} & \multirow{2}{*}{0.15974} & \multirow{2}{*}{0.90966} & \multirow{2}{*}{82.5} & \multirow{2}{*}{85.1} \\  & F-TP-Lm & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The correctness of the classification of technical state for the presented model. The best result was achieved for Model Number 12. In this model, a feed-forward network with backpropagation was used. One hidden layer with 10 neurons was applied. The tangential activation function (TANSIG) was used, as well as the linear activation function for the output layer (PURELIN). The Levenberg-Marquardt algorithm (TRAINNLM) was used as the learning algorithm. The best result was achieved for 12 epochs, with MSE = 0.15974, R = 0.90966, gradient = 0.1293, and Mu = 0.0001. For training, the accuracy was 0.91632, and for testing, the accuracy was 0.94019. In both tested _AI methods_, i.e., _AI Method 1_ (decision tree) and _AI Method 2_ (ANN model) the same structure of input and output data was used, but to develop a hybrid method it is not necessary to have the same structure of such data. For the hybrid method, the data structure which is the best for each AI method should be used.