Estimation of the parameters of one-dimensional maps from chaotic time series
Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Smirnov, D. A. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© Pleiades Publishing, Inc. 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Technical physics letters - Nauka/Interperiodica, 1993, 31(2005), 2 vom: Feb., Seite 97-100 |
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| Übergeordnetes Werk: |
volume:31 ; year:2005 ; number:2 ; month:02 ; pages:97-100 |
| Links: |
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| DOI / URN: |
10.1134/1.1877613 |
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| Katalog-ID: |
OLC2072869129 |
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| 520 | |a Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. | ||
| 650 | 4 | |a Time Series | |
| 650 | 4 | |a Objective Function | |
| 650 | 4 | |a Numerical Experiment | |
| 650 | 4 | |a Noise Level | |
| 650 | 4 | |a Accurate Estimate | |
| 700 | 1 | |a Vlaskin, V. S. |4 aut | |
| 700 | 1 | |a Ponomarenko, V. I. |4 aut | |
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10.1134/1.1877613 doi (DE-627)OLC2072869129 (DE-He213)1.1877613-p DE-627 ger DE-627 rakwb eng 530 VZ Smirnov, D. A. verfasserin aut Estimation of the parameters of one-dimensional maps from chaotic time series 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Inc. 2005 Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. Time Series Objective Function Numerical Experiment Noise Level Accurate Estimate Vlaskin, V. S. aut Ponomarenko, V. I. aut Enthalten in Technical physics letters Nauka/Interperiodica, 1993 31(2005), 2 vom: Feb., Seite 97-100 (DE-627)171149521 (DE-600)1158056-2 (DE-576)038488426 1063-7850 nnns volume:31 year:2005 number:2 month:02 pages:97-100 https://doi.org/10.1134/1.1877613 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_40 GBV_ILN_70 GBV_ILN_4700 AR 31 2005 2 02 97-100 |
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10.1134/1.1877613 doi (DE-627)OLC2072869129 (DE-He213)1.1877613-p DE-627 ger DE-627 rakwb eng 530 VZ Smirnov, D. A. verfasserin aut Estimation of the parameters of one-dimensional maps from chaotic time series 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Inc. 2005 Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. Time Series Objective Function Numerical Experiment Noise Level Accurate Estimate Vlaskin, V. S. aut Ponomarenko, V. I. aut Enthalten in Technical physics letters Nauka/Interperiodica, 1993 31(2005), 2 vom: Feb., Seite 97-100 (DE-627)171149521 (DE-600)1158056-2 (DE-576)038488426 1063-7850 nnns volume:31 year:2005 number:2 month:02 pages:97-100 https://doi.org/10.1134/1.1877613 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_40 GBV_ILN_70 GBV_ILN_4700 AR 31 2005 2 02 97-100 |
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10.1134/1.1877613 doi (DE-627)OLC2072869129 (DE-He213)1.1877613-p DE-627 ger DE-627 rakwb eng 530 VZ Smirnov, D. A. verfasserin aut Estimation of the parameters of one-dimensional maps from chaotic time series 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Inc. 2005 Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. Time Series Objective Function Numerical Experiment Noise Level Accurate Estimate Vlaskin, V. S. aut Ponomarenko, V. I. aut Enthalten in Technical physics letters Nauka/Interperiodica, 1993 31(2005), 2 vom: Feb., Seite 97-100 (DE-627)171149521 (DE-600)1158056-2 (DE-576)038488426 1063-7850 nnns volume:31 year:2005 number:2 month:02 pages:97-100 https://doi.org/10.1134/1.1877613 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_40 GBV_ILN_70 GBV_ILN_4700 AR 31 2005 2 02 97-100 |
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10.1134/1.1877613 doi (DE-627)OLC2072869129 (DE-He213)1.1877613-p DE-627 ger DE-627 rakwb eng 530 VZ Smirnov, D. A. verfasserin aut Estimation of the parameters of one-dimensional maps from chaotic time series 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Inc. 2005 Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. Time Series Objective Function Numerical Experiment Noise Level Accurate Estimate Vlaskin, V. S. aut Ponomarenko, V. I. aut Enthalten in Technical physics letters Nauka/Interperiodica, 1993 31(2005), 2 vom: Feb., Seite 97-100 (DE-627)171149521 (DE-600)1158056-2 (DE-576)038488426 1063-7850 nnns volume:31 year:2005 number:2 month:02 pages:97-100 https://doi.org/10.1134/1.1877613 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_40 GBV_ILN_70 GBV_ILN_4700 AR 31 2005 2 02 97-100 |
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Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. © Pleiades Publishing, Inc. 2005 |
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Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. © Pleiades Publishing, Inc. 2005 |
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Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method. © Pleiades Publishing, Inc. 2005 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2072869129</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504113758.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1134/1.1877613</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2072869129</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)1.1877613-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Smirnov, D. A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of the parameters of one-dimensional maps from chaotic time series</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Pleiades Publishing, Inc. 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The problem of constructing model maps based on the experimental chaotic time series is considered. A new method of estimation of the model parameters for one-dimensional maps is proposed, which employs a least squares procedure and calculations of the objective function using iterations of the model map in the reverse time. The results of a numerical experiment show that the proposed method provides much more accurate estimates than does the traditional approach at a moderate noise level below a certain threshold. The greater the number of parameters to be evaluated, the higher the threshold and, hence, the broader the domain of high efficiency of the new method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time Series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Objective Function</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Numerical Experiment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Noise Level</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Accurate Estimate</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vlaskin, V. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ponomarenko, V. I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Technical physics letters</subfield><subfield code="d">Nauka/Interperiodica, 1993</subfield><subfield code="g">31(2005), 2 vom: Feb., Seite 97-100</subfield><subfield code="w">(DE-627)171149521</subfield><subfield code="w">(DE-600)1158056-2</subfield><subfield code="w">(DE-576)038488426</subfield><subfield code="x">1063-7850</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:31</subfield><subfield code="g">year:2005</subfield><subfield code="g">number:2</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:97-100</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1134/1.1877613</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">31</subfield><subfield code="j">2005</subfield><subfield code="e">2</subfield><subfield code="c">02</subfield><subfield code="h">97-100</subfield></datafield></record></collection>
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