Generals cheme of complex systems models of self-organization algorithms construction using evolutionary search

Authors

  • V. F. Irodov State Higher Education Establishment “Pridneprovsk State Academy of Civil Engineering and Architecture”, Department of Heat Engineering and gas supply, Ukraine https://orcid.org/0000-0001-8772-9862
  • R. V Barsuk State Higher Education Establishment “Pridneprovsk State Academy of Civil Engineering and Architecture”, Department of Heat Engineering and gas supply, Ukraine https://orcid.org/0000-0002-9666-7496

Keywords:

inductive method of self-organization models of complex systems, evolutionary search, algorithm, similarity criterion, partial description

Abstract

.  Purpose. There are occur tasks in the mathematical modeling, the object of study which are complex systems. Convection, heat transfer and other are the objects examples of study. In addition, such systems could be difficult for study. Because of that, number of small experimental data often obtained. Therefore, the main question is to find mathematical model constructing variant with small amount of input data. The self-organization of mathematical models designed by A.G. Ivahnenko is chose.

Methodology. In comparison with other methods, such as analysis of variance, this method is relatively new. However, during its short stories, it has practical value already proved. The clarification of generating models gives by structural algorithm of self-organization that illustrated at Pic.1. “Partial description” has a special importance. It characterizes the complexity of the model and possibility of its using. Linear “private description” is using most commonly as it pointed in the article. However, considering the scope of the using this method, linear description cannot describe the object of study. Therefore, it proposed to use non-linear “partial description” for this method. In addition, there are examples of non-linear functions that could using during description of the object.

Findings. There are methods of evolutionary search for self-organization of mathematical models are developed. This is search reviewed more detailed in the article and shown a block-diagram of the algorithm at Pic.2. It is suggest using “partial description” in a nonlinear form. There are description to this algorithm and shown the importance of “partial description” form for building object of study a mathematical model.

Originality. There is developed construction of complex systems mathematical models general scheme by using self-organization with model parameters “partial description” evolutionary search. There is distinctive convergence with probability 1 process model parameters “partial description” evolutionary search.

Practical value. Models self-organization general scheme using evolutionary solutions search has been realized programmatically. It is indicated the possibility of these methods more widely using and increasing the simulation accuracy in comparison with existing methods

Author Biographies

V. F. Irodov, State Higher Education Establishment “Pridneprovsk State Academy of Civil Engineering and Architecture”, Department of Heat Engineering and gas supply

Dr. Sc. (Tech.), Prof.,

R. V Barsuk, State Higher Education Establishment “Pridneprovsk State Academy of Civil Engineering and Architecture”, Department of Heat Engineering and gas supply

Post Grad. St

References

Bukatova L.L., Mikhasov Y.I., Sharov A.M. Evoinformatika: Teoriya i praktika evolutsionnogo modelirovaniya [Evoinformatics : Theory and practice of evolutionary modeling]. Moscow, Nauka Publ., 1991. 203 p.

Ivahnenko A.G., Zaychenko Yu.P., Dimitrov V.D. Prinyatie resheniy na osnove samoorganizatsii [Adoption of decision based on self-organization]. Moscow, Sovetskoe radio Publ., 1976. 277 p.

Ivakhnenko A.G. Inductivnyy metod samoorganizatsii modeley slozhnikh sistem [The inductive method of selforganization models of complex systems]. Kiev, Naukova dumka Publ., 1982. 293 p.

Irodov V.F. O postroenii i skhodimosti algoritmov samoorganizatsii sluchaynogo poiska [The construction and convergence of random search algorithms for selforganization]. Avtomatyka – Automation, 1987, issue 4, pp. 34-43.

Kutateladze S.S. Spravochnik po teploperedache [Handbook of heat transfer]. Novosibirsk, Nauka Publ., 1970. 658 p.

Irodov V.F. Self-organization methods for analysis of nonlinear systems with binary choice relations / System analysis modeling simulation, 1995, vol. 18-19, pp. 203-206.

Farlow S. J. Self-organizing methods in modeling. Statistics : Textbook and monographs, 1984, vol. 54, pp. 45.

Frank L. Self-organization modeling for decision support. International conference in inductive modeling ICIM’2013. [Knowledge Miner Software]. Berlin, 172-178 p.

Godfrey C.O. Design of hybrid differential evolution and group method of data handling for inductive modeling.Information science, 2011, vol. 178, no. 18, pp. 87.

Mueller J. A., Lemke F. Self-organization data mining. An intelligent approach to extract knowledge from data. Berlin, 1999. 225 p.

Madala H.R, Ivakhnenko A.G. Inductive learning algorithms for complex systems modelling. London, CRC Press Inc. Boca Raton. Ann Arbor Publ., 1994. 250 p.

Wasniowski R.A. Using self-organization networks for intrusion detection. Proceedings of the 6th WSEAS int. conf. of Neural Networks. Lisbon, 2005, pp. 90-94.

Russel I. Neural networks module. Hartford, 2012. 115 p.

Turki Y.A., Abdulkareem A.Y., Lamya D.A. Financial prediction using inductive models. Basrah Journal of science, 2013, vol. 31 (2), pp. 64-72

Yasmeen M.Z. System identification using group method of data handling. Sharjah, 2009. 87 p.

Vikas L., Sanita L. An adapted group method of data handling for abrupt data analysis. International journal of engineering and computer science ISSN:2319-7242, 2014, vol. 3, no. 2, pp. 3842-3851.

Issue

Section

Computer systems and information technologies in education, science and management