# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 4 Experiments and Discussion
### Classification Experiment of Radiation Intensity Sequences
#### 4.2.1 Classification Experiment  
The infrared radiation intensity time series sample set of the spatial point target used in the experiment is obtained by modeling the real ballistic scene in Section 2, and the data length can be adjusted and changed. The classification performance of IRRNN algorithm can be tested at different moments in the case of data dynamic input. Considering that the observation of the target in the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{
\begin{tabular}{c} **Sequence** \\ **Length** \\ \end{tabular} } & \multicolumn{4}{c}{**Accuracy**} \\ \cline{3-6}  & & **FNNs** & **RNNs** & **LSTM** & **IRRNN** \\ \hline ECG200 & 96 & 0.8189 & 0.8433 & 0.8650 & 0.8974 \\ ArrowHead & 251 & 0.7495 & 0.8012 & 0.8166 & 0.8285 \\ SyntheticControl & 60 & 0.7230 & 0.7432 & 0.7843 & 0.7812 \\ OSULeaf & 427 & 0.5823 & 0.6059 & 0.6337 & 0.6828 \\ FaceAll & 131 & 0.5541 & 0.5722 & 0.5870 & 0.6931 \\ SwedishLeaf & 128 & 0.7419 & 0.7692 & 0.7705 & 0.8131 \\ FiftyWords & 270 & 0.3932 & 0.4239 & 0.5152 & 0.6549 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of four algorithms in UCR data set. space infrared sensor may be obscured, and the data may be missing, we set the target sample sequence to be randomly acquired from 120 s to 300 s, and the sequence of the interception time is 30 s. The classification performance of algorithms are observed at times of 8 s, 16 s, 24 s from the start point of each sequence. The experiment was divided into three groups, and the performance of the classification algorithm with the beginning time t beg of 150, 200, and 250 s was tested. Set the acquired sample data to a signal-to-noise ratio (SNR) level of 20 dB. The simulated infrared radiation intensity sequence sample data of the targets is 2000 sets, and the four types of targets each have 500 sets. Samples are randomly assigned to the training set, validation set and testing set according to the ratio of 2:1:1. So the number of samples for the training set is 1000, and the number of samples for the validation set and testing set is 500. In this experiment, a separate IndRNN structure(2 layers) and RRNN structure were added to compare with the IRRNN structure and its bi-directional extended structure B-IRRNN. The purpose is to compare the effects of these two structures on classification performance. Traditional RNNs were used as a reference. According to the classification algorithm performance in three groups of experiments as shown in Tables 3-5, the following conclusions can be obtained:  
(1) The classification accuracy of traditional RNNs algorithm improves with the increase of the sequence length. Because the RNN's network can store the state information of the previous states and accumulate the historical history as the sequence length increases and the accuracy of classification of time series is also improved. However, although traditional RNNs have the ability of time-delay memory, the problem of degradation in parameter learning still exists. Only the sequence information in local time can be learned, and the long-term dependence of the sequence cannot be learned. (2) IndRNN and RRNN have more advanced structures than traditional RNNs, and the classification performance is greatly improved compared with RNNs. The independent structure of IndRNN solves the problem of gradient vanishing and explosion, and can learn the long-term dependencies of sequences. For the RRNN structure, the historical output information uses the form of random weighting to make the new input data tend to the direction that is easy to classify. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Observing Time (s)**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{2-6}  & **RNNs** & **IndRNN** & **RRNN** & **IRRNN** & **B-IRRNN** \\ \hline \(8\) & 0.6503 & 0.7985 & 0.7946 & 0.8822 & 0.9048 \\ \(16\) & 0.7455 & 0.8393 & 0.8420 & 0.8995 & 0.9109 \\ \(24\) & 0.7917 & 0.8867 & 0.8851 & 0.9098 & 0.9272 \\ \hline \hline \end{tabular}
\end{table}