Standard

Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. / Chernykh, G.A.; Kuperin, Y.A.; Dmitrieva, L.A.; Navleva, A.A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689. Vol. 9719 Springer Nature, 2016. p. 108-114.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Chernykh, GA, Kuperin, YA, Dmitrieva, LA & Navleva, AA 2016, Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689. vol. 9719, Springer Nature, pp. 108-114, Advances in Neural Networks, St. Petersburg, Russian Federation, 6/06/16. https://doi.org/10.1007/978-3-319-40663-3_13

APA

Chernykh, G. A., Kuperin, Y. A., Dmitrieva, L. A., & Navleva, A. A. (2016). Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689 (Vol. 9719, pp. 108-114). Springer Nature. https://doi.org/10.1007/978-3-319-40663-3_13

Vancouver

Chernykh GA, Kuperin YA, Dmitrieva LA, Navleva AA. Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689. Vol. 9719. Springer Nature. 2016. p. 108-114 https://doi.org/10.1007/978-3-319-40663-3_13

Author

Chernykh, G.A. ; Kuperin, Y.A. ; Dmitrieva, L.A. ; Navleva, A.A. / Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13th International Symposium on Neural Networks, ISNN 2016; St. Petersburg; Russian Federation; 6 July 2016 through 8 July 2016; Code 177689. Vol. 9719 Springer Nature, 2016. pp. 108-114

BibTeX

@inproceedings{4b76cdf3ef2f4826b1b1431369b6f551,
title = "Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks",
abstract = "A method for calculating the largest Lyapunov exponents analogs for the numerical series obtained from acoustic experimental data is proposed. It is based on the use of artificial neural networks for constructing special additional series which are necessary in the process of calculating the Lyapunov exponents. The musical compositions have been used as acoustic data. It turned out that the error of the largest Lyapunov exponent computations within a single musical composition is sufficiently small. On the other hand for the compositions with different acoustic content there were obtained various numerical values Lyapunov exponents. This enables to make conclusion that the proposed procedure for calculating the Lyapunov exponents is adequate. It also allows to use the obtained results as an additional macroscopic characteristics of acoustic data for comparative analysis.",
author = "G.A. Chernykh and Y.A. Kuperin and L.A. Dmitrieva and A.A. Navleva",
year = "2016",
doi = "10.1007/978-3-319-40663-3_13",
language = "English",
isbn = "978-331940662-6",
volume = "9719",
pages = "108--114",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
address = "Germany",
note = "Advances in Neural Networks : 13th International Symposium on Neural Networks, ISNN 2016 ; Conference date: 06-06-2016 Through 08-06-2016",

}

RIS

TY - GEN

T1 - Calculation of Analogs for the Largest Lyapunov Exponents for Acoustic Data by Means of Artificial Neural Networks

AU - Chernykh, G.A.

AU - Kuperin, Y.A.

AU - Dmitrieva, L.A.

AU - Navleva, A.A.

PY - 2016

Y1 - 2016

N2 - A method for calculating the largest Lyapunov exponents analogs for the numerical series obtained from acoustic experimental data is proposed. It is based on the use of artificial neural networks for constructing special additional series which are necessary in the process of calculating the Lyapunov exponents. The musical compositions have been used as acoustic data. It turned out that the error of the largest Lyapunov exponent computations within a single musical composition is sufficiently small. On the other hand for the compositions with different acoustic content there were obtained various numerical values Lyapunov exponents. This enables to make conclusion that the proposed procedure for calculating the Lyapunov exponents is adequate. It also allows to use the obtained results as an additional macroscopic characteristics of acoustic data for comparative analysis.

AB - A method for calculating the largest Lyapunov exponents analogs for the numerical series obtained from acoustic experimental data is proposed. It is based on the use of artificial neural networks for constructing special additional series which are necessary in the process of calculating the Lyapunov exponents. The musical compositions have been used as acoustic data. It turned out that the error of the largest Lyapunov exponent computations within a single musical composition is sufficiently small. On the other hand for the compositions with different acoustic content there were obtained various numerical values Lyapunov exponents. This enables to make conclusion that the proposed procedure for calculating the Lyapunov exponents is adequate. It also allows to use the obtained results as an additional macroscopic characteristics of acoustic data for comparative analysis.

U2 - 10.1007/978-3-319-40663-3_13

DO - 10.1007/978-3-319-40663-3_13

M3 - Conference contribution

SN - 978-331940662-6

VL - 9719

SP - 108

EP - 114

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Nature

T2 - Advances in Neural Networks

Y2 - 6 June 2016 through 8 June 2016

ER -

ID: 8625443