A.V. Skatkov, A.A. Bryukhovetsky, D.V. Moiseev
Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33
The paper describes the adaptation of mechanisms of artificial immune systems for their use in environmental control systems. To be able to predict the state of the environment, the identification problem arises, which consists in finding the capacity of pollution sources based on available experimental data. As we know, artificial immune systems (AIS) are successfully used for optimization, classification and identification tasks.in addition, AIS are used for information compression, clustering, anomaly search, machine learning, unstructured data processing and information retrieval, computer security and adaptive control.
When predicting the state of the environment, the identification problem arises, which consists in finding the capacity of pollution sources based on available experimental data. As you know, artificial immune systems are successfully used to solve this kind of problems. the paper studies mathematical models of enhancing the immune response, artificial immune systems, which are systems of ordinary differential equations. For the first time, it is proposed to accumulate an anti-virus database ahead of time in order to increase the effectiveness of anti-virus measures. The analysis of the obtained results allows us to conclude that the adaptation of mechanisms of artificial immune systems by modifying classical mathematical models in accordance with the specifics of the AIS significantly increases their effectiveness in predicting the state of the environment. The development of artificial intelligence and unmanned control and monitoring tools built using it allows using the mechanisms of artificial immune systems to control environmental parameters in offline mode.
Keywords: artificial intelligence, unmanned control systems, artificial immune systems.
LIST OF REFERENCES
- Ushakov S.A., Astakhova I.F., Khitskova Yu.V. Using distributed artificial immune systems to solve the problem of identification in ecology // Modeling of systems and processes. 2016. No. 3. P. 10–14. DOI: https://doi.org/10.12737/17159.
- Stankevich L.A., Kazansky A.B. Immunological system for ensuring the safety of a humanoid robot // Current issues of protection and security: tr. 9-j Vseros. nauch.-prakt. konf., 2006. No. 5. P. 145–152.
- Garrett S.M. How do we evaluate artificial immune systems? How do we evaluate artificial immune systems? 2005. Vol. 13. P. 145–178.
- Hunt J.E., Cooke D.E. Learning using an artificial immune system // Journ. of Network Computing Applications, 1996. Vol. 19. P. 189–212.
- Knight T., Timmis J. Aine: An immunological approach to data mining // IEEE Intern. Conf. on Data Mining, 2001. P. 297304.
- Kim J., Bentley P. Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection. In Proc. Congress on Evolutionary Computation, Honolulu, HI, USA, 2002. P. 1244–1252.
- Yang H., Li T., Hu X., Wang F., Zou1 Y. A Survey of Artificial Immune System Based Intrusion Detection // The Scientific World Journal. 2014.
- Vtoryj V.F., Vtoryj S.V. Prospects for environmental monitoring of agricultural facilities using unmanned aerial vehicles // Technologies and technical means of mechanized production of crop and livestock products. 2017. No. 92. P. 158–166. doi:10.24411/0131-5226-2017-00028.
- Skatkov A.V., Bryukhovetskiy A.A. and Moiseev D.V. 2020 Adaptive vulnerability detection model for unmanned vehicles drugs based on artificial immune systems IOP Conference Series: Materials Science and Engineering 734 012028. DOI: iopscience.iop.org/article/10.1088/1757-899X/734/1/012028.
- Mathematical models in immunology. Computational methods and experiments / red. G.I. Marchuk. M.: Nauka, 1991. 299 p.
- Mathematical models in immunology and medicine / red. G.I. Marchuk. M.: Mir, 1986. 150 p.
- Marchuk G.I. Mathematical models in immunology / G.I. Marchuk. M.: Nauka, 1985. 240 p.
- Lugovskaya Yu.P. Mathematical modeling of optimal treatment processes for infectious diseases: avtoref. dis. … kand. fiz.-mat. nauk. Samara, 2009.
- Mathematical models in immunology and medicine: collection of articles / per. s angl. / pod red. G.I. Marchuka, L.N. Belyh. M.: Mir, 1986. 310 p.
- Belykh L.N., Marchuk G.I. Qualitative analysis of the simplest mathematical model of infectious disease // Mathematical modeling in immunology and medicine. Novosibirsk: Nauka, 1982. P. 5–26.