# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 4 Experiments and Discussion
### Classification Experiment of Radiation Intensity Sequences
#### 4.2.2 Effect of Noise and Sequence Length  
The experiment mainly tests the classification performance of four algorithms including RNNs, LSTM, IRRNN, and B-IRRNN under the situation of different noise levels and different sequence lengths. In the training process, all the algorithms are trained with the same signal-to-noise ratio that is SNR = 20 dB and the same sequence length L = 400. Classification performance of infrared radiation intensity time series of spatial point targets by different algorithms are tested under different signal-to-noise ratio levels (5 dB, 10 dB, 15 dB, 20 dB, 25 dB, and 30 dB) and different input sequence lengths (200, 400, 600, 800, and 1000). Ten independent Monte Carlo simulation experiments were carried out, and the mean of the classification accuracy of each algorithm was taken as the final result. As shown in Figure 7, we can get the following conclusions:  
Figure 7: Classification performance of four algorithms under the situations of different SNR sequence lengths.  
(1) With the increase of SNR, the classification accuracy of each algorithm is obviously improved, indicating that noise is an important factor affecting the classification task of spatial point target based on radiation intensity sequence. Therefore, the selected classification algorithm must be robust to noise. Compared with traditional RNNs and LSTM, the proposed IRRNN algorithm and B-IRRNN algorithm have obvious advantages, and even in the case of high noise level, they have more stable classification ability and verify the robustness to noise.  
(2) Comparing Figure 7a,b, it can be seen that the classification accuracy of four algorithms increases steadily with the increase of the sequence length, because the longer the sequence, the more periodic information of the target motion included and the more favorable to the classification of the sequence. It can be clearly seen from Figure 7c that when the sequence reaches a certain length, traditional RNNs and the LSTM cannot achieve effective classification of the sequence. This is because the problems of gradient vanishing and gradient explosion still exist, and their structure and activation function decided that a long sequence could not be processed. At this time, the advantage of the IRRNN algorithm and B-IRRNN algorithm are reflected. Their ability to process long sequences is strong, and the classification accuracy does not fluctuate greatly with the length of the sequence, remains at a relatively stable high level, and improves with the sequence length increases.  
In summary, the IRRNN algorithm can process long sequences due to the independent structure of its hidden layer, which is beneficial to capturing the long-term periodic features of spatial point targets. The historical information is introduced into the input through random weighting simultaneously, which effectively improves the comprehensive classification performance of the algorithm and enhances the generalization capabilities of the network. In addition, the independent structure of the IRRNN algorithm simplifies the network parameters and calculations, and the random weighting of historical information does not increase the learning complexity of the network. Therefore, the IRRNN algorithm can accomplish the classification easily and efficiently. For the infrared radiation intensity time series classification task of the spatial point target studied in this paper, IRRNN can process long time sequences, achieve stable classification accuracy, be robust to noise, and output classification results in real time, which is in accordance with classification task requirements.