TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA
Background. The statistical analysis of small samples in 16 experiments using the standard deviation estimation is considered. The aim of the work is the neural network prediction of errors in calculating standard deviations on small samples of biometric data. Materials and methods. Multi-layer ne...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2022-01-01
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| Series: | Измерение, мониторинг, управление, контроль |
| Subjects: |
| _version_ | 1811303464721973248 |
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| author | V.I. Volchikhin A.I. Ivanov E.A. Malygina S.V. Kachalin S.A. Polkovnikova |
| author_facet | V.I. Volchikhin A.I. Ivanov E.A. Malygina S.V. Kachalin S.A. Polkovnikova |
| author_sort | V.I. Volchikhin |
| collection | DOAJ |
| description | Background. The statistical analysis of small samples in 16 experiments using the standard deviation estimation
is considered. The aim of the work is the neural network prediction of errors in calculating standard deviations
on small samples of biometric data. Materials and methods. Multi-layer networks of artificial neurons were used to predict
the values of errors in calculating standard deviations. Deep neural network learning algorithms are well known. The main problem for their implementation is usually to obtain sufficiently large training samples. The novelty of the
approach lies in the fact that for the problem under consideration, an automatic machine for forming training samples
with different values of errors in estimating the standard deviation is used. Results. The created neural network error corrector
reduces the error interval for calculating the standard deviation by 22.7 % for samples of 16 experiments. At the same
time, the problem is revealed, which consists in the need to perform long-term training of multilayer neural networks for
each new volume of samples. Conclusion. The analysis of the results obtained in the course of the study showed that neural
network error correctors can increase the reliability of statistical estimates and other points. At the same time, neural network
predictors of errors in calculating mathematical expectations and correlation coefficients can be created. It is assumed
that the process of improving confidence will be monotonous and in one or two years it will be possible to reduce the uncertainty
interval of calculations by an additional 20 % by using networks of 15 or 20 layers of neurons. |
| first_indexed | 2024-04-13T07:48:33Z |
| format | Article |
| id | doaj.art-d39aa42217004297883a7c827814805e |
| institution | Directory Open Access Journal |
| issn | 2307-5538 |
| language | English |
| last_indexed | 2024-04-13T07:48:33Z |
| publishDate | 2022-01-01 |
| publisher | Penza State University Publishing House |
| record_format | Article |
| series | Измерение, мониторинг, управление, контроль |
| spelling | doaj.art-d39aa42217004297883a7c827814805e2022-12-22T02:55:37ZengPenza State University Publishing HouseИзмерение, мониторинг, управление, контроль2307-55382022-01-01410.21685/2307-5538-2021-4-8TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATAV.I. Volchikhin0A.I. Ivanov1E.A. Malygina2S.V. Kachalin3S.A. Polkovnikova4Penza State UniversityPenza State UniversityPenza State UniversityNPP "Rubin"Penza State UniversityBackground. The statistical analysis of small samples in 16 experiments using the standard deviation estimation is considered. The aim of the work is the neural network prediction of errors in calculating standard deviations on small samples of biometric data. Materials and methods. Multi-layer networks of artificial neurons were used to predict the values of errors in calculating standard deviations. Deep neural network learning algorithms are well known. The main problem for their implementation is usually to obtain sufficiently large training samples. The novelty of the approach lies in the fact that for the problem under consideration, an automatic machine for forming training samples with different values of errors in estimating the standard deviation is used. Results. The created neural network error corrector reduces the error interval for calculating the standard deviation by 22.7 % for samples of 16 experiments. At the same time, the problem is revealed, which consists in the need to perform long-term training of multilayer neural networks for each new volume of samples. Conclusion. The analysis of the results obtained in the course of the study showed that neural network error correctors can increase the reliability of statistical estimates and other points. At the same time, neural network predictors of errors in calculating mathematical expectations and correlation coefficients can be created. It is assumed that the process of improving confidence will be monotonous and in one or two years it will be possible to reduce the uncertainty interval of calculations by an additional 20 % by using networks of 15 or 20 layers of neurons.analysis of small samplesmultilayer network of artificial neuronsprediction of errors in calculating the standard deviation |
| spellingShingle | V.I. Volchikhin A.I. Ivanov E.A. Malygina S.V. Kachalin S.A. Polkovnikova TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA Измерение, мониторинг, управление, контроль analysis of small samples multilayer network of artificial neurons prediction of errors in calculating the standard deviation |
| title | TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA |
| title_full | TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA |
| title_fullStr | TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA |
| title_full_unstemmed | TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA |
| title_short | TESTING THE QUALITY OF NEURAL NETWORK ERROR CORRECTION FOR CALCULATING THE STANDARD DEVIATION OF SMALL SAMPLES OF BIOMETRIC DATA |
| title_sort | testing the quality of neural network error correction for calculating the standard deviation of small samples of biometric data |
| topic | analysis of small samples multilayer network of artificial neurons prediction of errors in calculating the standard deviation |
| work_keys_str_mv | AT vivolchikhin testingthequalityofneuralnetworkerrorcorrectionforcalculatingthestandarddeviationofsmallsamplesofbiometricdata AT aiivanov testingthequalityofneuralnetworkerrorcorrectionforcalculatingthestandarddeviationofsmallsamplesofbiometricdata AT eamalygina testingthequalityofneuralnetworkerrorcorrectionforcalculatingthestandarddeviationofsmallsamplesofbiometricdata AT svkachalin testingthequalityofneuralnetworkerrorcorrectionforcalculatingthestandarddeviationofsmallsamplesofbiometricdata AT sapolkovnikova testingthequalityofneuralnetworkerrorcorrectionforcalculatingthestandarddeviationofsmallsamplesofbiometricdata |