All issues

List of references:

1. В. В. Бухтояров. Трехступенчатый эволюционный метод формирования коллективов нейронных сетей для решения задач классификации // Программные продукты и системы. — 2012. — № 4. — С. 101–106.
   • V. V. Buhtoyarov. Three-step evolutionary method of forming teams of neural networks for solving classification problems // Software products and systems. — 2012. — no. 4. — P. 101–106. — in Russian.
2. К. В. Воронцов, Д. Ю. Каневский. Коэволюционный метод обучения алгоритмических композиций // Таврический вестник информатики и математики. — 2005. — № 2. — С. 51–66.
   • K. V. Voroncov, D. Yu. Kanevskij. Coevolutionary method of teaching algorithmic compositions // Tavrichesky herald of computer science and mathematics. — 2005. — no. 2. — P. 51–66. — in Russian.
3. Р. Б. Сергиенко. Метод формирования нечеткого классификатора самонастраивающимися коэволюционными алгоритмами // Искусственный интеллект и принятие решений. — 2010. — № 3. — С. 98–106.
   • R. B. Sergienko. The method of forming a fuzzy classifier by self-tuning coevolutionary algorithms // Artificial Intelligence and Decision Making. — 2010. — no. 3. — P. 98–106. — in Russian.
4. С. Д. Штовба. Идентификация нелинейных зависимостей с помощью нечеткого логического вывода в системе MATLAB // Математика в приложениях. — 2003. — № 2(2). — С. 9–15.
   • S. D. Shtovba. Identification of nonlinear relationships using fuzzy inference system in MATLAB // Mathematics in Applications. — 2003. — no. 2(2). — P. 9–15. — in Russian.
5. A. M. Akhmetvaleev, A. S. Katasev. Neural network model of human intoxication functional state determining in some problems of transport safety solution // Computer Research and Modeling. — 2018. — V. 10, no. 3. — P. 285–293. — DOI: 10.20537/2076-7633-2018-10-3-285-293.
6. K. Bache, M. Lichman. UCI Machine Learning Repository. — Irvine, CA: University of California, School of Information and Computer Science, 2013. — http://archive.ics.uci.edu/ml.
7. C. Ge, B. Wang, X. Wei, Y. Liu. Exponential synchronization of a class of neural networks with sampled-data control // Applied Mathematics and Computation. — 2017. — no. 315. — P. 150–161. — MathSciNet: MR3693461.
8. G. Guskov, A. Namestnikov, N. Yarushkina. Approach to the search for similar software projects based on the UML ontology // Advances in Intelligent Systems and Computing. — 2018. — no. 680. — P. 3–10. — DOI: 10.1007/978-3-319-68324-9_1.
9. I. I. Ismagilov, S. F. Khasanova, A. S. Katasev, D. V. Kataseva. Neural network method of dynamic biometrics for detecting the substitution of computer // Journal of Advanced Research in Dynamical and Control Systems. — 2018. — no. 10. — P. 1723–1728. — 10 Special Issue.
10. J. R. Jang, C. T. Sun. ANFIS: Adaptive-Network-based Fuzzy Inference Systems // IEEE Tranc. on Systems, Man and Cybernetics. — 1993. — V. 23. — P. 665–685. — DOI: 10.1109/21.256541.
11. A. S. Katasev, D. V. Kataseva. Expert diagnostic system of water pipes gusts in reservoir pressure maintenance processes / 2nd International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM. — Proceedings. — 2016. — 7911651.
12. A. S. Katasev, D. V. Kataseva, L. Yu. Emaletdinova. Neuro-fuzzy model of complex objects approximation with discrete output / 2nd International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM. — Proceedings. — 2016. — 7911653.
13. X. Liu, H. Wang, C. Gao, M. Chen. Adaptive fuzzy funnel control for a class of strict feedback nonlinear systems // Neurocomputing. — 2017. — no. 241. — P. 71–80. — DOI: 10.1016/j.neucom.2017.02.030.
14. A. M. Namestnikov, A. A. Filippov, V. S. Avvakumova. An ontology-based model of technical documentation fuzzy structuring // CEUR Workshop Proceedings. — 2016. — no. 1687. — P. 63–74.
15. J. Rauch. Expert deduction rules in data mining with association rules: a case study // Knowledge and Information Systems. — 2019. — no. 59(1). — P. 167–195. — DOI: 10.1007/s10115-018-1206-x.
16. E. Tron, M. Margaliot. Mathematical Modeling of Observed Natural Behavior: a Fuzzy Logic Approach // Fuzzy Sets and Systems. — 2004. — V. 146. — P. 437–450. — DOI: 10.1016/j.fss.2003.09.005. — MathSciNet: MR2082237.
17. N. Hassan, O. R. Sayed, A. M. Khalil, M. A. Ghany. Fuzzy Soft Expert System in Prediction of Coronary Artery Disease // International Journal of Fuzzy Systems. — 2017. — no. 19(5). — P. 1546–1559. — DOI: 10.1007/s40815-016-0255-0.
18. R. Schapire. The boosting approach to machine learning: An overview / MSRI Workshop on Nonlinear Estimation and Classification. — 2001. — 23 p. — Berkeley, CA. — MathSciNet: MR2005788.
19. R. B. Sergienko, E. S. Semenkin. Michigan and Pittsburgh Methods Combining for Fuzzy Classifier Generating with Coevolutionary Algorithm for Strategy Adaptation / Proc. of 2011 IEEE Congress on Evolutionary Computation. — 2011. — New Orleans, LA, USA.
20. L. Smaga. Bootstrap methods for multivariate hypothesis testing // Communications in Statistics: Simulation and Computation. — 2017. — no. 46(10). — P. 7654–7667. — DOI: 10.1080/03610918.2016.1248573. — MathSciNet: MR3764993.
21. M. Vaskovic, V. S. Kodogiannis, D. Budimir. An adaptive fuzzy logic system for the compensation of nonlinear distortion in wireless power amplifiers // Neural Computing and Applications. — 2018. — no. 30(8). — P. 2539–2554. — DOI: 10.1007/s00521-017-2849-3.
22. D. Wachla, W. A. Moczulski. Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases // Engineering Applications of Artificial Intelligence. — 2007. — no. 20(5). — P. 699–707. — DOI: 10.1016/j.engappai.2006.11.002. — MathSciNet: MR2711352.
23. N. Yarushkina. Soft computing and complex system analysis // International Journal of General Systems. — 2001. — no. 30(1). — P. 71–88. — DOI: 10.1080/03081070108960699.
24. L. A. Zadeh. Fuzzy Sets // Information and Control. — 1965. — V. 8. — P. 338–353.