# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
## 4 Results
### Results Generalizations
The auto-associative neural network (autoencoder) using effectiveness as part of the functional diagram for lost information restoration system of complex dynamic objects has been experimentally confirmed in comparison with other auto-associative neural network (autoencoder) models (convolutional autoencoder) and classical machine learning methods (linear regression, SVM, optimized SVM) according to these methods evaluation metrics such as precision, recall, F-measure, AUC, as well as training time: - When using an auto-associative neural network (autoencoder) with an improved basic algorithm for its training, the precision, recall, F-measure, and AUC metric values for normal data are 0.989, 0.987, 0.988, and 0.990, respectively, and for abnormal data--0.967, 0.967, 0.968, and 0.990, accordingly; when using a convolutional autoencoder, for normal data, they are 0.953, 0.949, 0.951, and 0.957, respectively, and for anomalous data--0.903, 0.902, 0.903, and 0.957, respectively, which is almost 3.33 to 6.81% more. At the same time, the training time for the auto-associative neural network (autoencoder) with an improved basic algorithm for its training was 5 min 43 s, and for the convolutional autoencoder--38 min 16 s, which indicates a reduction in training time and, as a consequence, improvement in lost information restoration by 6.69 times. - When using an auto-associative neural network (autoencoder) with an improved basic algorithm for its training, the precision, recall, F-measure, and AUC metric values for normal data are 0.989, 0.987, 0.988, and 0.990, respectively, and for abnormal data--0.967, 0.967, 0.968, and 0.990, accordingly; when applying a model based on linear regression, for normal data, they are 0.772, 0.622, 0.594, and 0.654, respectively, and for abnormal data--0.685, 0.522, 0.466, and 0.654, respectively, which is almost 21.94 to 51.86% more. At the same time, the training time for an auto-associative neural network (autoencoder) with an improved basic algorithm for its training was 5 min 43 s, and for a model based on linear regression--2 min 11 s. Although models based on linear regression train 2.62 times faster than auto-associative neural network models (autoencoder), their use in lost information restoration tasks for complex dynamic objects reduces accuracy by 21.94 to 51.86%. - When using an auto-associative neural network (autoencoder) with an improved basic algorithm for its training, the precision, recall, F-measure, and AUC metric values for normal data are 0.989, 0.987, 0.988, and 0.990, respectively, and for abnormal data--0.967, 0.967, 0.968, and 0.990, accordingly; when using a model based on the SVM, for normal data, they are 0.902, 0.903, 0.902, and 0.904, respectively, and for abnormal data--0.788, 0.781, 0.784, and 0.904, respectively, which is almost 8.51 to 19.23% more. At the same time, the training time for an auto-associative neural network (autoencoder) with an improved basic algorithm for its training was 5 min 43 s, and for a model based on the SVM--6 min 11 s, which indicates a reduction in training time and, as a consequence, improvement in restoring lost information by 1.08 times. - When using an auto-associative neural network (autoencoder) with an improved basic algorithm for its training, the precision, recall, F-measure, and AUC metric values for normal data are 0.989, 0.987, 0.988, and 0.990, respectively, and for abnormal data--0.967, 0.967, 0.968, and 0.990, accordingly; when using a model based on the optimized SVM, for normal data, they are 0.949, 0.943, 0.945, and 0.948, respectively, and for abnormal data--0.795, 0.798, 0.793, and 0.948, respectively, which is almost 4.04 to 18.08% more. At the same time, the training time of the auto-associative neural network (autoencoder) with an improved basic algorithm for its training was 5 min 43 s. The model based on the optimizedSVM was 18 min 24 s, which indicates a reduction in training time and, as a consequence, improvement in lost information restoration by 3.22 times.