# Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks
## 3 SQ Evaluation Model of EVs
### GA-BP ANN Model  
#### 3.2.1 GA-BP ANN  
Given the complicated nonlinear correlation of the objective parameters and subjective perception, the artificial neural network (ANN) technique can be used to identify these relationships between the objective parameters and the subjective SQ perception. The back propagation artificial neural network (BP-ANN) can theoretically approximate any function with arbitrary precision to achieve complex nonlinear mapping, which can better solve complex nonlinear problems that are difficult to model with mathematical formulas [21]. Although the BP-ANN has many advantages and is widely used, it still has many shortcomings and defects [22; 23; 24]. For example, the robustness is poor, and the network performance is very sensitive to the initial weight setting of the network. The BP-ANN can also easily fall into the local minimum, so it cannot obtain the global optimal solution [25; 26].  
The genetic algorithm (GA) is a kind of global optimization algorithm established by simulating biological genetic and evolution processes with good global characteristics [27]. It can be used to optimize the BP-ANN, make it have self-evolution, and improve its adaptive ability so as to construct a BP-ANN with a global search ability [28].  
Therefore, this study used the GA to optimize the weights and thresholds of the BP-ANN and then build a GA-BP ANN. It also used the global search capability of the GA algorithm to find the weight and threshold corresponding to the initial suboptimal solution of the network, which were then used as the initial weight and threshold of the network. Afterwards, the BP algorithm was used to train the network, and it could well prevent the network from falling into the local minimum, improved the convergence speed, and made the network get rid of its dependence on the initial value. The specific operation flow chart of the GA-BP ANN is shown Figure 12, and the specific steps of the algorithm are as follows: (1) determining the initial structure and parameters of the GA-BP ANN; (2) determining the GA operation, setting the parameters, and encoding; (4) using the obtained optimization weight as the initial network weight and then using the BP algorithm to train the network; and (5) outputting the final result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Determination Coefficient R\({}^{2}\)**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standard Coefficient**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig**} \\ \cline{5-6} \cline{6-7}  & & & **B** & & **Standard Error** & & \\ \hline
1 & Constant & \multirow{2}{*}{0.876} & 10.277 & 0.111 & \multirow{2}{*}{0.937} & 92.826 & 0 \\  & Loudness & & \(-\)0.142 & 0.006 & \(-\)0.937 & \(-\)23.02 & 0 \\
2 & Constant & & 10.761 & 0.139 & \multirow{2}{*}{77.147} & 0 \\  & Loudness & & \(-\)0.905 & \(-\)0.116 & 0.008 & \(-\)0.766 & \(-\)15.23 & 0 \\  & Sharpness & & \(-\)0.760 & 0.157 & \(-\)0.243 & \(-\)4.830 & 0 \\
3 & Constant & & 11.575 & 0.259 & & 44.703 & 0 \\  & Loudness & & \(-\)0.078 & 0.013 & \(-\)0.517 & \(-\)6.231 & 0 \\  & Sharpness & & \(-\)1.023 & 0.163 & \(-\)0.327 & \(-\)6.285 & 0 \\  & FS & & \(-\)20.92 & 5.774 & \(-\)0.232 & \(-\)3.625 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the regression analysis.