Modeling of Dynamic Systems With Interval Parameters

245

Abstract

The paper provides a review of existing libraries and methods of modeling dynamic systems with interval parameters. Available software libraries AWA, VNODELP, COZY Infinity, RiOT, FlowStar, as well as the author’s adaptive interpolation algorithm are considered. The traditional software for interval analysis gives guaranteed estimates of solutions, however, over time, these estimates become extremely significantly overstated. Due to the use of a fundamentally different approach to constructing solutions, the adaptive interpolation algorithm is not subject to the accumulation of errors, determines the boundaries of solutions with controlled accuracy, and works much faster than analogues.

General Information

Keywords: interval methods, dynamic systems with interval parameters, adaptive interpolation algorithm, libraries with methods, AWA, VNODE, COSY Infi nity, RiOT, FlowStar, verifyode

Journal rubric: Mathematical Modelling

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2019090401

For citation: Morozov A.Y., Reviznikov D.L. Modeling of Dynamic Systems With Interval Parameters. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2019. Vol. 9, no. 4, pp. 5–31. DOI: 10.17759/mda.2019090401. (In Russ., аbstr. in Engl.)

References

  1. Pavlov B.M., Novikov M.D. Avtomatizirovannyj praktikum po nelinejnoj dinamike (sinergetike) [Automated workshop on nonlinear dynamics (synergetics)]. – Dialog MGU, VMK, 2000. – 115 p.
  2. Krasilnikov P.S. Prikladnye metody issledovaniya nelinejnyh kolebanij [Applied methods for studying nonlinear oscillations]. M. – Izhevsk: Institut komp’yuternyh issledovanij [Institute for Computer Research], 2015. – 528 p.
  3. Rybakov K.A., Rybin V.V. Modelirovanie ra spredelennyh i drobno-raspredelennyh processov i sistem upravleniya spektral’nym metodom [Modeling of distributed and fractionally distributed processes and spectral method control systems]. M.: Izd-vo MAI [Publishing House of the Moscow Aviation Institute], 2016. – 160 p.
  4. Panteleev A.V., Rybakov K.A. Prikladnoj veroyatnostnyj analiz nelinejnyh sistem upravleniya spektral’nym metodom [Applied probabilistic analysis of nonlinear spectral method control systems]. M.: Izd-vo MAI-PRINT [Publishing house MAI-PRINT], 2010. – 160 p.
  5. Arkhipov A.S., Semenikhin K.V. Garantirujushhee ocenivanie parametrov odnomernoj modeli dvizhenija po verojatnostnomu kriteriju pri nalichii unimodal’nyh pomeh [A guaranteeing estimation of the parameters of a one-dimensional motion model according to a probabilistic criterion in the presence of unimodal int erference] // Modelirovanie i analiz dannyh [Modeling and data analysis]. 2019. No 2. Pp.31–38.
  6. Sobo l’ I.M. Metod Monte-Karlo [Monte Carlo Method]. M.: Nauka, 1978.
  7. Ermakov S.M., Mihajlov G.A. Statisticheskoe modelirovanie [Statistical Modeling]. M.: Nauka, 1982.
  8. Kramer G. Matematicheskie metody statistiki [Mathematical Statistics Methods]. M.: Mir, 1975.
  9. Zolotarev V.M. Obshchaya teoriya peremnozheniya nezavisimyh sluchajnyh velichin [General Theory of Multiplication of Independent Random] // Doklady AN SSSR. V. 142. № 4. 1962. Pp. 788–791.
  10. Young R.C. The algebra of many-valued quantities // Mathematische Annalen. Vol. 104. 1931. P. 260–290.
  11. Dwyer P.S. Linear Computations. New York: John Wiley & Sons, 1951.
  12. Warmus M. Calculus of Approximations // Bulletin de l’Academie Polonaise de Sciences. Vol. 4. № 5. 1956. P. 253–259.
  13. Sunaga T. Theory of an Interval Algebra and its Application to Numerical Analysis // RAAG Memoirs. Vol. 2. 1958. P. 547–564.
  14. Moore R.E. Interval Analysis. Englewood Cliffs: Prentice Hall, 1966.
  15. Lohner R.J., Enclosing the solutions of ordinary initial and boundary value problems // Computer Arithmetic: Scientifi c Computation and Programming Languages. 1987. P. 255–286.
  16. Hansen E. Interval Arithmetic in Matrix Computations Part I // SIAM Journal on Numerical Analysis. Vol. 2, № 2. 1965. P. 308–320.
  17. Alefel’d G., Hercberger YU. Vvedenie v interval’nye vyc hisleniya [Introduction to Interval Computing]. M.: Mir [World], 1987.
  18. Krawczyk R. Newton-Algorithmen zur Bestimmung von Nullstellen mit Fehlerschranken // Computi ng. Vol. 4. № 3. 1969. P. 187–201.
  19. Nickel K. Über die Notwendigkeit einer Fehlerschranken-Arithmetic für Rechenautomaten // Numerisch e Mathematik. Vol. 9. № 1. 1966. P. 69–79.
  20. Neumaier A. Interval Methods for Systems and Equations. Cambridge: Cambridge University Press, 1990.
  21. Bradis V.M. Teoriya i praktika vychislenij. Posobie dlya vysshih pedagogicheskih uchebnyh zavedenij [Theory and practice of computing. Manual for higher pedagogical educational institutions]. M.: Uchpedgiz, 1937.
  22. Kantorovich L.V. O nekotoryh novyh podhodah k vychislitel’nym metodam i obrabotke nablyudenij [About some new approaches to computational methods and processing of observations] // Sibirskij matematicheskij zhurnal [Siberian Mathematical Journal] V. 3. № 5. 1962. Pp. 701–709.
  23. Kalmykov S.A., SHokin YU.I., YUldashev Z.H. Metody interval’nogo analiza [Interval Analysis Methods]. Novosibirsk: Nauka, 1986.
  24. Shokin Yu.I. Interval’nyj analiz [Interval analysis]. M.: Nauka, 1981.
  25. Dobronets B.S., Popova O.A. Chislennyj verojatnostnyj analiz neopredelennyh dannyh [Numerical probabilistic analysis of uncertain data]. Krasnojarsk: Sib. feder. un-t [Krasnoyarsk: Siberian Federal University], 2014. 168 p.
  26. Dobronets B.S. Interval’naja matematika. [Interval math]. Krasnojarsk: Krosnojar. gos. un-t [Krasnoyarsk: Krasnoyarsk State University]. 2007.
  27. Shary S.P. Konechnomernyj interval’nyj analiz [Finite dimensional interval analysis]. Novosibirsk: XYZ, 2017.
  28. Rogalev A.N. Garantirovannye metody reshenija sistem obyknovennyh differencial’nyh uravnenij na osnove preobrazovanija simvol’nyh formul [Guaranteed methods for solving systems of ordinary differential equations based on the transformation of symbolic formulas] // Vychislitel’nye tehnologii [Computational technologies]. Vol. 8. No. 5. 2003. Pp. 102–116.
  29. Rogalev A.N. Voprosy realizacii garantirovannyh metodov vkljuchenija vyzhivajushhih traektorij upravljaemyh sistem [Implementation issues on guaranteed methods for including surviving trajectories of controlled systems] // Sibirskij zhurnal nauki i tehnologij [Siberian Science and Technology Journal]. No. 2 (35). 2011. Pp. 54–58.
  30. Rogalev A.N. Issledovanie i ocenka reshenij obyknovennyh differencial’nyh uravnenij interval’no-simvol’nymi metodami. [Research and evaluation of solutions of ordinary differential equations by interval-symbolic methods] // Vychislitel’nye tehnologii [Computational technologies]. Vol. 4. No. 4. 1999. Pp. 51–75.
  31. Morozov A.Yu., Reviznikov D.L. Modifi kacija metodov reshenija zadachi Koshi dlja system obyknovennyh differencial’nyh uravnenij s interval’nymi parametrami [Modifi cation of methods for solving the Cauchy problem for systems of ordinary differential equations with interval parameters] // Trudy MAI [Transactions of MAI]. No. 89.2016. Pp. 1–20.
  32. Eijgenraam P. The Solution of Initial Value Problems Using Interval Arithmetic: Formulation and Analysis of an Algorithm. Amsterdam : Mathematisch Centrum, 1981. P. 185.
  33. Lohner R.J. Einschließung der Losung gewohnlicher Anfangs und Randwertaufgaben und Anwendungen. PhD thesis, Universitat Karlsruhe, 1988.
  34. Nedialkov N.S., Jackson K.R., Pryce J.D. An effective high-order interval method for validating existence and uniqueness of the solution of an IVP for an ODE // Reliable Computing, Vol. 7. № 6. 2001. P. 449–465.
  35. Stauning O. Automatic Validation of Numerical Solutions. PhD thesis, Technical University of Denmark, 1997.
  36. Lin Y., Stadtherr M.A. Validated solutions of initial value problems for parametric ODEs. // Applied Numerical Mathematics. Vol 57. № 10. 2007. P. 1145–1162
  37. Nedialkov N.S. VNODE-LP – a validated solver for initial value problems in ordinary differential equations. Technical Report CAS-06–06-NN, Department of Computing and Software, McMaster University, 2006.
  38. Berz M., Makino K. Verifi ed integration of ODEs and fl ows with differential algebraic methods on Taylor models // Reliable Computing. Vol. 4. № 4. 1998. P. 361–369.
  39. Berz M., Makino K. Suppression of the wrapping effect by Taylor model-based verifi ed integrators: long-term stabilization by shrink wrapping // Differential Equations and Applications. Vol. 10. № 4. 2005. P. 385–403.
  40. Berz M., Makino K. Suppression of the wrapping effect by Taylor model-based verifi ed integrators: long-term stabilization by preconditioning // Differential Equations and Applications. Vol. 10. № 4. 2005. P. 353–384.
  41. Makino K., Berz M. Effi cient control of the dependency problem based on Taylor model methods // Reliable Computing. Vol. 5. № 1. 1999. P. 3–12.
  42. Makino K., Berz M. Taylor models and other validated functional inclusion methods // Pure and Applied Mathematics Vol. 4. № 4. 2003. P. 379–456.
  43. Makino K., Berz M. Verifi ed Computations Using Taylor Models and Their Applications // Numerical Software Verifi cation 2017: conference proceedings. (Heidelberg, Germany, July 22–23, 2017). Springer International Publishing AG 2017. P. 3–13.
  44. Neher M., Jackson K.R., Nedialkov N.S., On Taylor Model Based Integration of ODEs // SIAM Journal on Numerical Analysis, Vol. 45. № 1. 2007. P. 236–262.
  45. Nataraj P.S. V., Sondur S., The Extrapolated Taylor Model // Reliable Computing, Vol. 15. 2011. P. 251–278.
  46. Pozin A.V. Obzor metodov i instrumental’nyh sredstv reshenija zadachi Koshi dlja ODU s garantirovannoj ocenkoj pogreshnosti [A review of methods and tools for solving the Cauchy problem for an ordinary differential equation with a guaranteed error estimate] // Mezhdunarodnaja konferencija «Sovremennye problemy prikladnoj matematiki i mehaniki: teorija, jeksperiment i praktika» [International Conference “Modern Problems of Applied Mathematics and Mechanics: Theory, Experiment and Practice”], May 30 – June 4, 2011, Novosibirsk Abstracts. Information and Computing Centre of the Siberian Branch of the Russian Academy of Sciences, 2011.
  47. Berz M. COSY INFINITY version 8 reference manual. Technical Report MSUCL–1088, National Superconducting Cyclotron Lab., Michigan State Universitz, 1997.
  48. Eble I. Über Taylor-Modelle: Dissertation zur erlangung des akademischen grades eines doktors der naturwissenschaften, Karlsruhe Institute of Technology, 2007.
  49. Chen X., Sankaranarayanan S. Decomposed Reachability Analysis for Nonlinear Systems. // 2016 IEEE Real-Time Systems Symposium (RTSS): conference proceedings. (Porto, Portugal, 29 Nov.-2 Dec. 2016). P. 13–24.
  50. Chen X., Abraham E., Sankaranarayanan S. FLOW*: An Analyzer for Non-linear Hybrid Systems // Proceedings of the 25th International Conference on Computer Aided Verifi cation. (Saint Petersburg, Russia, July 13–19, 2013), Springer-Verlag New York, Vol. 8044, p. 258–263.
  51. Rump S.M. INTLAB – INTerval LABoratory. In Tibor Csendes, editor, Developments in Reliable Computing, Kluwer Academic Publishers, Dordrecht, 1999, p. 77–104.
  52. Makino K., Berz M. Rigorous Reachability Analysis and Domain Decomposition of Taylor Models // Numerical Software Verifi cation 2017: conference proceedings. (Heidelberg, Germany, July 22–23, 2017). Springer International Publishing AG 2017, p. 90–97.
  53. Kletting M., Rauh A., Aschemann H., Hofer E.P., Consistency tests in guaranteed simulation of nonlinear uncertain systems with application to an activated sludge process // Computational and Applied Mathematics. Vol. 199. № 2. 2007. P. 213–219.
  54. Dobronets B.S. On some two-sided methods for solving systems of ordinary differential equations // Interval Computation. 1992. Vol. 1. № 3. P. 6–19.
  55. Dobronets B.S., Roshchina E.L. Prilozhenija interval’nogo analiza chuvstvitel’nosti. [Applications of interval sensitivity analysis] // Vychislitel’nye tehnologii [Computational technologies]. Vol. 7. Number 1. 2002. Pp. 75–82.
  56. Nekrasov S.A. Effi cient Two-Sided Methods for the Cauchy Problem in the Case of Large Integration Intervals // Differential Equations, 2003, V. 39, I. 7, Pp 1023–1027.
  57. Chernousko F.L. Ocenivanie fazovyh sostojanij dinamicheskih sistem. [Estimation of phase states of dynamical systems]. Metod jellipsoidov [Ellipsoid method]. M.: Nauka, 1988. 319 p.
  58. Kurzhanski А. В., Vdlyi I. Ellipsoidal Calculus for Estimation and Control. SCFA. Boston, 1997.
  59. Morozov A.Yu., Reviznikov D.L. Adaptive interpolation algorithm based on a kd-tree for numerical integration of systems of ordinary differential equations with interval initial conditions. Differential Equations, 2018, Vol. 54, No. 7, p. 945–956.
  60. Morozov A.Y., Reviznikov D.L., Gidaspov V.Y. Adaptive Interpolation Algorithm Based on a kd-Tree for the Problems of Chemical Kinetics with Interval Parameters // Mathematical Models and Computer Simulations, 2019, V. 11, I. 4, pp 622–633
  61. Morozov A. Yu., Reviznikov D.L. Modelling of dynamic systems with interval parameters on graphic processors // Programmnaja inzhenerija. Vol. 10. No 2. 2019. Pp. 69–76.
  62. Morozov A.Yu. Programma dlja chislennogo integrirovanija sistem obyknovennyh differencial’nyh uravnenij s interval’nymi nachal’nymi uslovijami [A program for the numerical integration of systems of ordinary differential equations with interval initial conditions] // Svidetel’stvo o gosudarstvennoj registracii programmy dlja JeVM [Certifi cate of state registration of a computer program] No. 2016464623 dated November 20, 2018
  63. Panteleev A.V., Skavinskaja D.V. Metajevristiches kie algoritmy global’noj optimizacii [Global optimization metaheuristic algorithms] M.: Vuzovskaja kniga [University book]. 2019., 332 p.
  64. Hansen E., Walster G.W., Global Optimization Using Interval Analysis. New York: Marcel Dekker, 2004.
  65. Panteleev A.V., Panovskiy V.N. Interval methods of global constrained optimization. Interval Analysis: Introduction, Methods and Applications. Nova Science Publishers, Inc. 2017. pp. 33–119.
  66. Panteleev A.V., Panovskij V.N. Obobshchennyj inversnyj interval’nyj metod global’noj uslovnoj optimizacii [Generalized inverse interval method of global conditional optimization] // Nauchnyj vestnik Moskovskogo gosudarstvennogo tekhnicheskogo universiteta grazhdanskoj aviacii [Scientifi c Bulletin of Moscow State Technical University of Civil Aviation]. № 207. 2014. Pp. 17–24.
  67. Krasnikov S.D., Kuznecov E.B. Metod prohozhdeniya tochek bifurkacii korazmernosti tri [A method for traversing bifurcation points of codimension three] // Prikladnaya matematika i mekhanika (Ul’yanovsk) [Applied Mathematics and Mechanics (Ulyanovsk)]. № 9. 2011. s. 335–346.
  68. Kuznecov E.B., Leonov S.S. Parametrization of the Cauchy problem for systems of ordinary differential equations with limiting singular points. Computational Mathematics and Mathematical Physics. V.57. Pp. 931–952.
  69. Neher M. Interval methods and Taylor model methods for ODEs // Workshop Taylor Model Methods VII, 14–17 december 2011 y. Florida. Abstracts. MSU 2011, P. 17.
  70. Makino K., Berz M.: Suppression of the wrapping effect by Taylor model – based validated integrators: MSU HEP Report 40910, 2003.
  71. Bünger F. Shrink wrapping for Taylor models revisited // Numerical Algorithms. № 4. 2018. P. 1–18.

Information About the Authors

Alexander Y. Morozov, PhD in Physics and Matematics, Researcher, Department 27 "Mathematical Modeling of Heterogeneous Systems", Federal Research Center Computer Science and Control of the Russian Academy of Sciences, Moscow, Russia, ORCID: https://orcid.org/0000-0003-0364-8665, e-mail: morozov@infway.ru

Dmitry L. Reviznikov, Doctor of Physics and Matematics, Professor, Federal State-Financed Educational Institution of Higher Professional Education "Moscow aviation Institute (national research University)", Moscow, Russia, e-mail: reviznikov@gmail.com

Metrics

Views

Total: 883
Previous month: 13
Current month: 8

Downloads

Total: 245
Previous month: 3
Current month: 1