# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 5 Model Training of Tunnel Lighting ANN
### Process of Model Training
#### 5.1.2 Training Experiments  
In this research, the actual lighting regulation scenario under normal weather conditions, unidirectional tunnel, design speed 80 km/h, and traffic volume 380 [veh/(h-ln)], is firstly selected to define and train the lighting neural network. However, since the fitting degree of this neural network architecture with regulation strategy is not a priori, it is necessary to compare the topologies of different neural networks (mainly referring to the number of hidden layers or number of neurons in each hidden layer) under this scenario. The training process is briefly described below:  
1. After one-hot processing, input and output parameters are both transformed into 3-degree data.
2. Before training starts, 2000 data labels are divided into 1500 training sets, 300 test sets, and 200 validation sets.
3. When training with Matlab, a neural network is created with the following statement: \[net=newff('train\_input','train\_output',[10,xx,xx], {'logsig'},'trainglx')\] (13) where, '\(train\_Input'\) and '\(train\_out'\) are each 1500 \(\times\) 3 arrays, formed by 1500 training sets. '\(logsig'\) is the transfer function (Logarithmic sigmoid transfer function). '\(trainglx'\) as the training function (gradient descent with momentum and adaptive learning rate backpropagation).
4. Training parameters are set as in Table 16.
5. In this premise, different attributes of the hidden layer are adjusted, and two groups of tests are performed, as Tables 17 and 18 denote.  
Experimental data of above two groups of tests are summarized as follows:  
1. When the number of hidden layers (depth) is extended, with increase in this number, network regression result and fitting degree of luminance and color temperature are basically similar, with no significant changes, but they can all meet the fitting target of color temperature and luminance (R > 0.995).
2. When the dimension of hidden layer is extended (1 hidden layer is maintained), the regression change is not obvious when the number of neurons is greater than five (both are greater than 0.995). However, when the number of neurons is 10, the fitting degree of luminance and color temperature is better than that when the number of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Meaning** \\ \hline net.trainParam.epochs & 5000 & Maximum number of epochs to train \\ \hline net.trainParam.goal & \(1\times 10^{-12}\) & Performance goal \\ \hline net.trainParam.lr & 0.01 & Learning rate \\ \hline net.trainParam.max\_fail & 6 & Maximum validation failures \\ \hline net.trainParam.mc & 0.9 & Momentum constant \\ \hline net.trainParam.showWindow & true & Show training GUI \\ \hline \hline \end{tabular}
\end{table}
Table 16: Training parameters assigned in the algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Hidden Layers** & **Neuron Number in Hidden Layers** & **Comparison (Regression, Luminance and CT Fitting)** \\ \hline \hline
1 & 1 & 10 & R \textgreater{} 0.996 for training, test and validation sets. \\  & & & Luminance curve fits well, bigger deviation \\  & & & takes place for lower O Luminance labels. \\  & & & CT achieves a 100\% recognition rate. \\ \hline
2 & 2 & 10, 10 & Nearly same targets to above. \\ \hline
3 & 3 & 10, 10, 10 & Nearly same targets to above. \\ \hline \hline \end{tabular}
\end{table}
Table 17: Test-1: Extend the depth of hidden layers (number of hidden layers).  
neurons is 5 or 20. Especially in the case of low luminance label, the deviation degree of fitting is better than the other two cases.
3. In the two tests, the fitting between the actual luminance of the lighting system and the predicted value of the trained neural network basically meets the following function (Equation (14)), that is, the predicted and actual values basically conform to unary linear regression with only slight deviations for \(a\) and \(b\). \[Output=a*Target+b\] (14)
4. Both tests achieved a 100% recognition rate for color temperature.