# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
###### Abstract
This shows the significant performance of an ANN under the context change and variation conditions in image parameters, indicating their potential importance in real-time face recognition and identification tasks. Based on the above, it can be stated that ANNs can be used for information restoration tasks. This direction is closely related to object tracking and recognition because they all describe working with incomplete or damaged data. After analyzing [15; 16; 17; 18; 19], information restoration tasks examples using ANNs were systematized:  
1. Image restoration: ANN can be used to restore damaged or incomplete images, such as those with noise, compression artifacts, or blur. 2. Denoising algorithms can sometimes smooth out not only noise but also fine details in the data, and other types of denoising can lead to blurring where sharp edges and textures are lost, making the data less accurate. ANNs can be used to remove noise from images or signals. 3. Interpolation can create false patterns or structures that do not exist in the original data. ANNs can be used to fill in gaps in data, for example, to create higher-resolution images. 4. Super resolution: ANNs can enhance low-quality image fidelity. At the same time, the ANN has the following advantages for information restoration tasks:  
1. Efficiency: ANNs can be very efficient compared with traditional information restoration methods. 2. Flexibility: ANNs can be adapted to different types of data and tasks. 3. Training: ANNs can automatically train from examples, making them more robust to noise and other data corruption. The lost information restoration task set in this work is relevant taking into account the above. It consists of the fact that in the modern technical progress conditions and the technical systems' complexity, ensuring their reliability and uninterrupted functioning becomes critically important. Because sensor failures are one of the common causes of accidents and system failures, the development of effective dynamic control and monitoring systems becomes an urgent need. This research aims to develop a complex nonlinear technical object system (including helicopter TE) for monitoring and operation control. It will ensure the lost information restoration in case of sensor failure. This will ensure the object's operation continuity and reliability in abnormal situations. The research's main tasks include the measured data analysis and processing using nonlinear dynamics methods for accurate monitoring and the method development for rapid detection and localization of sensor failures. Also, this will create the system that quickly restores the sensor's performance to ensure the uninterrupted operation of the facility. The research results are relevant for applied system innovation development by offering a new method for solving the information restoration task when sensors fail. The research results will be relevant to researchers, engineers, and developers of complex management and control systems, industrial automation, robotics, intelligent systems, information technology, cybersecurity, etc., using applied system innovation. The scientific novelty of the obtained results is the development of a neural network method for lost information restoration in complex dynamic object sensor failure cases, which, through the auto-associative neural network (autoencoder) use, makes it possible to restore lost data in sensor failures with almost 100% accuracy.