METHODS FOR DETECTION OF ANOMALOUS MEASUREMENTS IN REAL-TIME SYSTEMS BASED ON MACHINE LEARNING ALGORITHMS
In article the problem of converting the measurement data through the communication channels in real-time systems. It lies in the fact that the transmission of information is subject to errors (anomalies) that may influence decision-making in a negative way, affect the validity of dynamical systems. To detect these anomalies are applied machine learning techniques. However, the measurement data in a temporary space cannot be applied. In this paper proposes a method of converting data to continue to make the processing of machine learning methods for the detection of anomalies.