Voronezh, Voronezh, Russian Federation
Artificial immune system is a complex of mathematical methods to simulate the basic func-tions of the human immune system, and used to determine the parameters and (or) their values that can minimize the impact of certain factors (external or internal) to the production and economic ent-ity. The main characteristic that distinguishes the immune system of a foreign agent is an antigen that is any molecule which can be recognized by cellular elements of immunity (lymphocytes) using specific sensitive receptors. Otherwise, the antigen is a separate index that distinguishes foreign agent. Despite all this, there are examples where this approach fails. There are cases where the im-mune system does not work on "friend or foe", but uses a protective mechanism of hazard recogni-tion, which is a key method of the theory of danger. This theory does not deny the existence of dif-ferentiation in the "friend or foe", and argues that there are other factors that lead to the initiation of the immune response. For example, the theory of danger determines the nature of data on the beha-vior of competing industrial and economic systems, which must be submitted and processed in the artificial immune systems. Application of the theory of danger increases the efficiency of mathe-matical models, forming an artificial immune system of the market, which in its turn allows recog-nition of a new competitor in the market, assess the risk on its part for the competitors, and deter-mine the values of the characteristics of companies that will dominate over the parameters of a new competitor.
market, market barriers, immune system, artificial immune systems, theory of danger.
1. Galaktionov, V.G. Immunologiya [Tekst] : ucheb. / V.G. Galaktionov. - 3-e izd. - M. : Akademiya, 2004. - 528 s.
2. Iskusstvennye immunnye sistemy i ikh primenenie [Tekst] / pod red. D. Dasgupty. Per. s angl. pod red. A. A. Romanyukhi. - M. : FIZMATLIT, 2006. - 344 s.
3. Hofmeyr, S. 2000, ´Architecture for an Artificial Immune System´ [Text] / Hofmeyr S. and Forrest S. - Evolutionary Computation. - 8 (4). - C. 443-473.
4. Venter, H. 2003, ´A Taxonomy for Information Security Technologies´ [Text] / Venter, H. and Eloff J. - Computers & Security. - 22 (4). - C. 299-307.
5. Cayzer, S. Recommender System based on the Immune Network [Text] / S. Cayzer, U.A. Aickelin. - Proceedings of the 2002 Congress on Evolutionary Computation, 2002.
6. Serbulov, Yu.S. Modelirovanie rynochnykh bar´erov na osnove iskusstvennoy im-munnoy sistemy [Tekst] / Yu.S. Serbulov, L.V. Stepanov, D.A. Glukhov. Lesotekhnicheskiy zhurnal. - 2013. - № 4 (12). - S. 223-231.
7. Litvinenko, V.I. Komp´yuternaya sistema dlya resheniya zadach klassifikatsii na osnove modifitsirovannykh immunnykh algoritmov [Tekst] / V.I. Litvinenko, A.A. Didyk, Yu.A. Zakharchenko. Informatsionno-izmeritel´nye sistemy. - 2008. - № 2 (22).
8. Brownlee, J. Clonal Selection Theory & CLONALG - The Clonal Selection Classification Algorithm (CSCA) [Text] / J. Brownlee. Victoria, Australia: Centre for Intelligent Systems and Complex Processes (CISCP), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology; 2005 Jan; Technical Report ID: 2-01.
9. Litvinenko, V.I. Immunnyy klassifikator dlya resheniya zadach binarnoy klassifikatsii (teoreticheskie osnovy) [Tekst] / V.I. Litvinenko. Sistemni tekhnologi. Regional´niy mizhvuzivs´kiy zbirnik naukovikh prats´. - Dnipropetrovs´k, 2006. - Vipusk 1 (42). - C. 114-130.
10. Matzinger, P. 2002, ´The Danger Model: A Renewed Sense of Self [Text] / P. Matzinger. - Science, 296. - 301-305.
11. Bardachev, Yu.N. Ispol´zovanie polozheniy teorii opasnosti v iskusstvennykh immunnykh sistemakh [Tekst] / Yu.N. Bardachev, A.A. Didyk. Informatsionno-upravlyayushchie kompleksy i sistemy. - 2007. - № 2 (20).
12. Litvak, B.G. Razrabotka upravlencheskogo resheniya [Tekst] : ucheb. / B.G. Litvak. - 3-e izd., ispr. - M. : Delo, 2002. - 392 s.
