Neural net without deep learning: signal approximation by multilayer perceptron

Славутский, Л. А.; Lazareva, N. N.; Portnov, Mikhail; Slavutskaya, Elena

doi:10.1117/12.2669233

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Many studies have been devoted to modeling such processes [9][10][11][12][13]. Such signal changes monitoring can be carried out in real time [14][15]. Artificial neural networks (ANN) can be used to solve this problem [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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The study is devoted to the possibility of using neural network algorithms to monitor the current and voltage signals parameters during transients in electrical engineering complexes. The direct propagation neural network can be programmed in standard microprocessor equipment and allows approximating signals in real time in the "sliding time window" mode. The proposed approach is demonstrated in laboratory experiments on the asynchronous motors run-out physical model as a complex load node part. It is shown that the multilayer perceptron use makes it possible to track changes in the signals frequency and amplitude with percentage accuracy for the observation time window duration of no more than a industrial frequency quarter period. The results demonstrate the using neural networks advantages for signal processing in non-stationary modes in electrical engineering and electric power industry.

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Section: Introductionmentioning

confidence: 99%

Neural net monitoring of signals parameters during the induction motors run-out

Andreev,

Grigoriev,

Malinin

et al. 2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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Error analysis of machine learning methods as the educational background for its use skills formation

Slavutskaya,

Slavutskii

2024

Vestnik of Minin University

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Introduction. Machine learning methods and elements of artificial intelligence are used to analyze random data, processes and signals. The study of relevant tools is already included in the various levels curricula. The purpose of the study is to demonstrate, using examples available to students of various specialties, that the error analysis of machine learning methods in solving specific tasks can be the basis in the educational process for the skills formation of using artificial intelligence elements.Materials and Methods. For processing random signals and data, widely available software is used: Microsoft Excel for preparing training and test samples, the Deductor analytical platform for implementing machine learning algorithms. As an example, quasi-harmonic signals with random parameters are processed for technical specialties, and the results of psycho diagnostics are used to process multidimensional random data.Results. As a typical solution of approximation technical problems, direct propagation neural network errors in using to determine random signal parameters are analyzed. As a solution of classification problems, multidimensional random data with different dimensions were processed using neural networks and the "decision tree" method. The advantages of the combined use of these two machine learning methods are analyzed. These examples and their analysis were tested in classes with university students in the disciplines of "Digital Signal Processing" and "Fundamentals of Statistics".Discussion and Conclusions. The statistical features of the obtained results, the possibilities of reducing the training sample and selective analysis of multidimensional random data are discussed. It is shown that an adequate assessment of the machine learning methods errors can significantly expand the possibilities of their application, and can be the basis for the formation of skills for their use.

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