System analysis of multidimensional random data is one of the most important tasks of modern information technologies. Traditional statistical methods for analyzing such data do not always allow one to obtain adequate results. In this article, the joint use of the decision tree method and the artificial neural networks is proposed for data classification. It is shown that this approach can be an alternative to multiple factorization of multidimensional data and has a number of significant advantages over traditional statistical methods. Even when the data has limited and different digital scales, the use of machine learning makes it possible to conduct a hierarchical classification of intra-system data relationships, to reduce the number of parameters significant for analysis, and significantly reduce the size of the learning sample required. For the technical applications, this allows for real-time data analysis using a microprocessor implementation of neural network algorithms.
The research is devoted to the use of artificial neural networks (ANN) for signal processing. The features of the simplest feed forward neural networks (multilayer perceptrons, MLP) application are analyzed. When using MLP in a sliding time window, it allows to solve problems of signal approximation with high accuracy and to determine their parameters when analyzing dynamic processes. If the signal can be set by analytical formulas with random parameters on separate time intervals, then after training, MLP can be implemented in microprocessor equipment for real-time signal processing. The ANN training algorithms and the errors of the proposed signal processing method are discussed. The approach does not require "deep learning" and a complex ANN structure, it allows one to control the accuracy of algorithms at intermediate stages of calculations. The results are of interest for electrical engineering and smart energy systems.
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