Current Derivative Estimation of Non-stationary Processes Based on Metrical Information; Computational Collective Intelligence; Vol. 9330 of the series Lecture Notes in Computer Science
| Parent link: | Computational Collective Intelligence Vol. 9330 of the series Lecture Notes in Computer Science.— 2015.— [P. 512-519] |
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| Summary: | Title screen Demand for estimation of derivatives has arisen in a range of some applied problems. One of the possible approaches to estimating derivatives is to approximate measurement data. The problem of real-time estimation of de-rivatives is investigated. A variation method of obtaining recurrent smoothing splines is proposed for estimation of derivatives. A distinguishing feature of the described method is recurrence of spline coefficients with respect to its segments and locality about measured values inside the segment. Influence of smoothing spline parameters on efficiency of such estimations is studied. Comparative analysis of experimental results is performed. Режим доступа: по договору с организацией-держателем ресурса |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1007/978-3-319-24306-1_50 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=645945 |
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| 200 | 1 | |a Current Derivative Estimation of Non-stationary Processes Based on Metrical Information |f E. A. Kochegurova, E. Gorokhova | |
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| 300 | |a Title screen | ||
| 320 | |a [References: 11 tit.] | ||
| 330 | |a Demand for estimation of derivatives has arisen in a range of some applied problems. One of the possible approaches to estimating derivatives is to approximate measurement data. The problem of real-time estimation of de-rivatives is investigated. A variation method of obtaining recurrent smoothing splines is proposed for estimation of derivatives. A distinguishing feature of the described method is recurrence of spline coefficients with respect to its segments and locality about measured values inside the segment. Influence of smoothing spline parameters on efficiency of such estimations is studied. Comparative analysis of experimental results is performed. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Computational Collective Intelligence | ||
| 463 | |t Vol. 9330 of the series Lecture Notes in Computer Science |v [P. 512-519] |d 2015 | ||
| 610 | 1 | |a электронный ресурс | |
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| 700 | 1 | |a Kochegurova |b E. A. |c specialist in the field of Informatics and computer engineering |c associate Professor of Tomsk Polytechnic University, candidate of technical Sciences |f 1958- |g Elena Alekseevna |3 (RuTPU)RU\TPU\pers\33442 |9 17123 | |
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