# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### Comparison of HPR Results of Several Methods  
Through the above comparison experiments, the structural parameters and training parameters finally determined for multilayer Bi-LSTM are shown in Table 2.  
In order to further verify the effectiveness of the proposed method in HPR tasks, we compare multilayer Bi-LSTM with traditional machine learning methods (MLP) and several deep learning methods (DBN and 1D-CNN). These methods are described in detail as follows:  
1. MLP: There are three layers of MLP used in the experiment. The first layer of MLP is the input layer with 25 neurons, which are used to receive the frequency of 25 intervals of histogram. The second layer is the hidden layer with 30 neurons. The last layer is the output layer with seven neurons, corresponding to seven typical HPs. The activation function of all layers of MLP is Sigmoid function. 2. DBN: The input of DBN is the frequency of 25 intervals of histogram. The DBN in this experiment is composed of three layers of RBM. Each RBM layer is pre-trained unsupervised. After the layer by layer pre-training is completed, the supervised global optimization is carried out. Each RBM layer contains 60 neurons, and the activation function of all layers is the Sigmoid function. 3. 1D-CNN: The 1D-CNN used in this experiment is the structure recommended in [18]. The input of 1D-CNN is the frequency of 25 intervals of histogram. It consists of two convolution layers, two pooling layers and a full connection layer, and they are all one-dimensional. The number of feature maps of the two convolution layers is 6 and 12 respectively, and the convolution kernel size is 2*1 and 9*1, respectively. The activation function of the convolution layer was the rectified linear units (ReLU) function, and the output layer was the Softmax function. In order to observe the results of pattern recognition more intuitively, confusion matrix is used. The values of the elements on the diagonal of the confusion matrix respectively represent the proportion of the correct recognition of various patterns. Other values represent the misjudgment of the classifier. The total CRR is equal to the average of all elements on the diagonal [1]. The confusion matrix of various identification methods is shown in Figure 9, and the total CRR and the time consumption of each epoch in the training process are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Learning Rate** & **Optimization Algorithm** & **Batch Size** & **Number of Layers** \\ \hline
0.05 & Adam & 200 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The structural parameters and training parameters of multilayer Bi-LSTM. Figure 9 shows that there are different levels of confusion in the recognition results of several HPR methods. However, the performance of three recognition methods based on deep learning is better than that of traditional machine learning methods. In the recognition results of several methods, there is a trend of confusion from the NOR pattern to the island pattern. This problem is the most serious in MLP method, followed by DBN method. This may be due to the similarity between the NOR pattern and the island pattern. This is an unacceptable result, which means that the HPR system based on these methods will have a very serious Type I error (false alarm), which will bring unnecessary trouble to the quality control work of the enterprise. On the contrary, the method based on 1-DCNN effectively improves the recognition results of nor pattern, and proves the effectiveness of processing one-dimensional data and the ability to capture the details of data. Due to the multilayer and bidirectional structure, the training time of multilayer Bi-LSTM is a little longer, but it gets a very