Informatics and Applications
December 2013, Volume 7, Issue 4, pp 3-10
STUDY OF THE DYNAMICS OF MULTIDIMENSIONAL STOCHASTIC SYSTEMS BASED ON ENTROPY MODELING
- A.N. Tyrsin
- O. V. Vorfolomeeva
Abstract
A new entropy approach of modeling of dynamics of stochastic systems is described. It is based on the representation of the system in the form of a multidimensional stochastic vector. It is shown that the change in entropy of a multivariate stochastic system can be expressed in terms of dispersions and conditional correlations of a component of a random vector. This allows to reveal the cause of the change in the entropy of the system and to evaluate it quantitatively. It was found that the entropy of a stochastic system consists of two components that characterize its properties. The first component determines the limit entropy corresponding to the full independence of the elements of the system and defines the consideration of the integral object as consisting of components (additivity). The second component reflects the extent of interrelation between the elements of the system, defining the properties of the system as a whole (integrity). This approach makes it possible to use entropy models in the diagnostics and control of stochastic systems as well as efficient management. The advantages of the proposed approach include the simplicity of implementation and interpretation of the mathematical model, the universality and adaptability for stochastic systems of different nature, the possibility of its use on small samples of data. The article contains an example of the practical application of a mathematical model.
[+] References (18)
- Klimontovich, Ju. L. 2002. Vvedenie v fiziku otkrytykh system [Introduction to the physics of open systems]. Moscow: Janus-K Publ. 284 p.
- Wilson A.G. 1970. Entropy in urban and regionalmodeling. L.: Pion. 166 p.
- Trubeckov, D. I., E. S. Mchedlova, and L. V. Krasichkov. 2002. Vvedenie v teoriyu samoorganizatsii otkrytykh system [Introduction to the theory of self-organization of open systems]. Moscow: Publishing House of Physical-Mathematical Literature. 200 p.
- Romanovskij, Ju.M., N. V. Stepanova, and D. S. Chernavskij. 2003. Matematicheskoe modelirovanie v biofizike [Mathematical modeling in biophysics]. Moscow-Izhevsk: Computer Research Institute. 402 p.
- Prangishvili, I. V. 2003. Entropiynye i drugie sistemnye zakonomernosti: Voprosy upravleniya slozhnymi sistemami [Entropic and other system regularities: Questions of management of complex systems]. Moscow: Nauka. 428 p.
- Skorobogatov, S.M. 2009. Katastrofy i zhivuchest' zhelezobetonnykh sooruzheniy (klassifikaciya i elementy teorii)[Catastrophes and serviceability of reinforced concrete buildings (classification and elements of theory)]. Ekaterinburg: Ural State University of Railway Transport. 512 p.
- Shannon C. 1948. Amathematical theory of communication. Bell Syst. Tech. J. 27(3):379-423; 4:623-56.
- Cover, T.M., and J.A. Thomas. 1991. Elements of information theory. N.Y.: Wiley. 563 p.
- Pena, D.,A. Van derLinde. 2007. Dimension less measures of variability and dependence for multivariate continuous distributions. Commun. Stat. Theor. M. 36(10):1845-54.
- Novickij, P. V. 1968. Osnovy informatsionnoj teorii izmeritel'nykh ustroystv [Fundamentals of information theorymeasuring devices]. Leningrad: Energy Publs. 248 p.
- Stratonovich, R. L. 1975. Teoriya informatsii [Information theory]. Moscow: Soviet Radio Publs. 424 p.
- Greene, W.H. 2011. Econometric analysis. 7th ed. Prentice Hall. 1230 p.
- Tyrsin, A.N., and I. S. Sokolova. 2012. Entropiyno - veroyatnostnoe modelirovanie gaussovskih stohasticheskikh sistem [Entropy-propabilistic modeling of Gaussian stochastic systems]. Matematicheskoe modelirovanie [Mathematical modeling] 24(1):88-102.
- Nikolis, G., and I. Prigogine. 1977. Self-oraganization in nonequilibrium systems: From dissipative structures to order through fluctuations. N.Y.: John Wiley&Sons. 512 p.
- Beirlant, J., E. J. Dudewicz, L. Gyorfi, and E.C. van derMeulen. 1997. Nonparametric entropy estimation: an overview. Int. J. Math. Stat. Sci. 6(1):17-39.
- Stowell, D., and M.D. Plumbley. 2009. Fast multidimensional entropy estimation by k-d partitioning. IEEE Signal Proc. Lett. 16(6):537-40.
- Tyrsin, A.N., and I.A. Kljavin. 2010. Povyshenie tochnosti otsenki entropii sluchaynyh eksperimental'nykh dannykh [Increase in the accuracy of the estimate of the entropy of a random experimental data]. Sistemy Upravleniya i Informatsionnye Tekhnologii [Control Systems and Information Technologies] 1(39):87-90.
- Noughabi, H.A., and N.R. Arghami. 2010. A new estimator of entropy. J. Iran. Stat. Soc. 9(1):53-64.
[+] About this article
Title
STUDY OF THE DYNAMICS OF MULTIDIMENSIONAL STOCHASTIC SYSTEMS BASED ON ENTROPY MODELING
Journal
Informatics and Applications
December 2013, Volume 7, Issue 4, pp 3-10
Cover Date
2013-12-31
DOI
10.14357/19922264130401
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
multidimensional random variable; entropy; dynamics; stochastic system; dispersion; correlation
Authors
A. N. Tyrsin , O. V. Vorfolomeeva
Author Affiliations
Science and Engineering Center "Reliability and Resource of Large Systems and Machines," Ural Branch, Russian Academy of Sciences, Yekaterinburg
Chelyabinsk State University
|