Article information

2020 , Volume 25, ¹ 3, p.46-53

Voskoboinikov Y.E., Boeva V.A.

Stable algorithm of nonparametric identification in case of anomalous measurements

Volterra integral equation of the first kind often represents stationary dynamic systems. For such a model, the non-parametric identification problem reduces to the estimation of pulse transition characteristics (that is the kernel of integral equation) from the registered noise-contaminated values of input and output signals. To formulate stable solution for identification problem authors propose algorithm that estimates pulse transition characteristics by solving Volterra integral equation of the second kind and involving first derivatives of input and output signals application that corresponds to non-stable problem. Smoothing cubic splines employed in robust calculation of first derivatives allow finding a stable solution of identification problem even when input and output signals of system identified are essentially noise-contaminated. Unfortunately, measured values of input and output signals also contain anomalous measurements such as pulse noises, glitches, etc. Such measurements are poorly smoothable by splines that cause high levels of first derivatives errors and, conversely, significant pulse transition characteristics identification errors of dynamic system.

For all the reasons aforementioned, in this paper authors present the new stable two-step identification algorithm in case of anomalous measurements. The first step of the algorithm is for non-linear local-spatial combined filtration procedure of input and output signals that helps to effectively remove anomalous measurements. At the second step, smoothing cubic splines are used to calculate stable first derivatives of previously filtered signals. An extensive computational experiment showed the effectiveness of the proposed algorithm, which allows solving the identification problem with acceptable accuracy in practice even at high intensity of anomalous measurements. The experimental results give reason to recommend this algorithm for solving practical problems of identifying stationary systems, the mathematical model of which is the Voltaire integral equation of the first kind with a difference kernel

[full text] [link to elibrary.ru]

Keywords: identification problem, algorithm for solving the Volterra II equation, filtration of anomalous measurements, smoothing cubic splines

doi: 10.25743/ICT.2020.25.3.006

Author(s):
Voskoboinikov Yuriy Evgenievich
Dr. , Professor
Position: Head of Chair
Office: Novosibirsk State University of Architecture and Civil Engineering, Novosibirsk State Technical University
Address: 630008, Russia, Novosibirsk, Leningradskaya Str., 113
E-mail: voscob@mail.ru
SPIN-code: 5803-4259

Boeva Vasilisa Andreevna
Position: Student
Office: Novosibirsk State University of Architecture and Civil Engineering
Address: 630008, Russia, Novosibirsk, Leningradskaya Str., 113
E-mail: v.boyeva@sibstrin.ru
SPIN-code: 2515-7089

References:

1. Sidorov D.N. Metody analiza integral’nykh dinamicheskikh modeley: teoriya i prilozheniya [Analysismethods of integral dynamic models: theory and applications]. Irkutsk: IGU; 2013: 293. (In Russ.)

2. Boykov I.V. Analiticheskie i chislennye metody identifikatsii dinamicheskikh system [Analytical andnumerical methods for identification of dynamic systems]. Penza: PGU; 2016: 396. (In Russ.)

3. Tikhonov A.N., Arsenin V.Ya. Metody resheniya nekorrektnykh zadach [Methods for solution of incorrect problems]. Moscow: Nauka; 1986: 285. (In Russ.)

4. Voskoboynikov Yu.E. Matematicheskaya obrabotka eksperimenta v molekulyarnoy gazodinamike[Mathematical processing of experiment in molecular gas dynamics]. Novosibirsk: Nauka; 1984: 238. (In Russ.)

5. Voskoboynikov Yu.E., Boeva V.A. New stable algorithm of nonparametric identification for technicalsystems. Sovremennye naukoemkie tekhnologii. 2019; (5):25–29. (In Russ.)

6. Zavyalov Yu.S., Kvasov B.I., Miroshnichenko V.L. Metody splayn-funktsiy [Methods of spline functions]. Moscow: Nauka; 1980: 352. (In Russ.)

7. Wang Y. Smoothing splines methods and applications. BOOK SERIES: Chapman & Hall/CRC Monographs on Statistics and Applied Probability. 2011; (121): 347.

8. Wahba G. Smoothing noisy data with spline functions. Numerische Mathematik. 1975; 24(2):383–393.

9. Voskoboinikov Yu.E., Krysov D.A. Estimation of the noise measurement characteristics in the model“Signal+Noise”. Automatics and Software Enginery. 2018; 3(25):54–61. (In Russ.)

10. Voskoboynikov Yu.E. Method of removal of wavelet filtration artifacts. Fundamental Research. 2017;(8-2):246–250. (In Russ.)

Bibliography link:
Voskoboinikov Y.E., Boeva V.A. Stable algorithm of nonparametric identification in case of anomalous measurements // Computational technologies. 2020. V. 25. ¹ 3. P. 46-53
Home| Scope| Editorial Board| Content| Search| Subscription| Rules| Contacts
ISSN 1560-7534
© 2024 FRC ICT