Systems and Means of Informatics
2020, Volume 30, Issue 1, pp 4-19
FILTERING AND EXTRAPOLATION IN MIGRATIONAL-POPULATIONAL STOCHASTIC SYSTEMS
- I. N. Sinitsyn
- V. I. Sinitsyn
Abstract
Approximate quasi-linear filtering and extrapolation methods for migrational-populational stochastic systems (MPStS) are developed. Volterra StS are the special case of MPStS. The MPStS are described by nonlinear differential Ito stochastic equations with additive and parametric noises. Corresponding algorithms are based on the conditionally-optimal linear Pugachev filtering and extrapolation theory. For wide-band noises, simplified approaches for filters synthesis are based on interchange of parametric noises by the additional ones. For narrow-band noise, the methods of Pugachev canonical expansions and generalized canonical expansions corresponding algorithms are proposed. As the test example, three-dimensional differential MPStS with nonlinear stochastic migrational flow with polarized additive and parametric noises is considered.
Some special cases are treated. Basic generalizations: (i) nonpolarized and autocorrelated noises in discrete and mixed continuous-discrete MPStS; and (ii) nonlinear filtering and extrapolation in MPStS.
[+] References (8)
- Sinitsyn, I.N., and V.I. Sinitsyn. 2019. Uslovno-optimal'noe lineynoe otsenivanie normal'nykh protsessov v vol'terrovskikh stokhasticheskikh sistemakh [Conditionally optimal linear estimation of normal processes in Volterra stochastic systems]. Sistemy
i Sredstva Informatiki - Systems and Means of Informatics 29(3):16-28.
- Sinitsyn, I.N., and V.I. Sinitsyn. 2018. Analiticheskoe modelirovanie normal'nykh protsessov v vol'terrovskikh stokhasticheskikh sistemakh [Analytical modeling of normal processes in Volterra stochastic systems]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 28(2):4-19.
- Sinitsyn, I.N., and V.I. Sinitsyn. 2018. Analiticheskoe modelirovanie raspredeleniy s invariantnoy meroy v vol'terrovskikh stokhasticheskikh sistemakh [Analytical modeling of distributions with invariant measure in Volterra stochastic systems]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 28(3):4-25.
- Sinitsyn, I.N., and V.I. Sinitsyn. 2019. Analiticheskoe modelirovanie protsessov v vol'terrovskikh stokhasticheskikh sistemakh metodom kanonicheskikh razlozheniy [Analytical modeling of processes in Volterra stochastic systems by the canonical expon- sions method]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 29(1): 109-127.
- Sinitsyn, I.N. 2007. Fil'try Kalmana i Pugacheva [Kalman and Pugachev filters]. Moscow: Logos. 776 p.
- Pugachev V. S., and I.N. Sinitsyn. 2001. Stochastic systems. Theory and applications. Singapore: World Scientific. 908 p.
- Sinitsyn, I. N. 2009. Kanonicheskiepredstavleniyasluchaynykhfunktsiyiikhprimenenie v zadachakh komp'yuternoy podderzhki nauchnykh issledovaniy [Canonical expansions of random functions and their applications in computer-aided support]. Moscow: Torus Press. 768 p.
- Sinitsyn, I.N., O.V. Druzhinina, and O.N. Masina. 2018. Analiticheskoe modeli- rovanie i analiz ustoychivosti nelineynykh shirokopolosnykh migratsionnykh potokov [Analytical modeling and stability analysis of nonlinear broadband migration flows]. Nelineynyy mir [Nonlinear World] 16(3):3-16.
[+] About this article
Title
FILTERING AND EXTRAPOLATION IN MIGRATIONAL-POPULATIONAL STOCHASTIC SYSTEMS
Journal
Systems and Means of Informatics
Volume 30, Issue 1, pp 4-19
Cover Date
2020-05-30
DOI
10.14357/08696527200101
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
analytical modeling; filtering and extrapolation; migrational- populational StS (MPStS); normal approximation method (NAM); Pugachev conditionally-optimal filtering and extrapolation; statistical linearization method; stochastic system (StS); Volterra StS
Authors
I. N. Sinitsyn  and V. I. Sinitsyn
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science
and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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