1 Applied Mathematics and Mechanics in Aerospace Industry
9
28. Panteleev, A.V., Pis’mennaya, V.A.: Application of a memetic algorithm for the optimal control
of bunches of trajectories of nonlinear deterministic systems with incomplete feedback. J.
Comput. Syst. Sci. Int. 57(1), 25–36 (2018)
29. Chugai, K.N., Kosachev, I.M., Rybakov, K.A.: Approximate Filtering Methods in ContinuousTime Stochastic Systems. In: Jain, L.C., Favorskaya, M.N., Nikitin, I.S., Reviznikov, D.L.
(eds.) Advances in Computational Mechanics and Numerical Simulation. Smart Innovation,
Systems and Technologies, vol. 173, pp. 351–371. Springer, Singapore (2020)
30. Averina, T.A., Rybakov, K.A.: Using maximum cross section method for filtering jumpdiffusion random processes. Rus. J. Numer. Anal. Math. Model.ing 35(2), 55–67 (2020)
31. Kuzmina, N.M., Ridley, A.N.: A method for estimating the impact of the fare rules conditions.
Civ. Aviat. High Technol. 224(2), 138–146 (2016)
32. Bochkov, A.V., Zhigirev, N.N., Ridley, A.N.: Method of recovery of priority vector for
alternatives under uncertainty or incomplete expert assessment. Dependability 17(3), 41–48
(2017)
33. Morozov, AYu., Reviznikov, D.L.: Adaptive interpolation algorithm based on a kd-tree
for numerical integration of systems of ordinary differential equations with interval initial
conditions. Differ. Eqn. 54(7), 945–956 (2018)
34. Morozov, AYu., Reviznikov, D.L., Gidaspov, VYu.: Adaptive interpolation algorithm based
on a kd-tree for the problems of chemical kinetics with interval parameters. Math. Models
Comput. Simul. 11(4), 622–633 (2019)
35. Rybakov, K.A.: Modeling and analysis of output processes of linear continuous stochastic
systems based on orthogonal expansions of random functions. J. Comput. Sys. Sc. Int. 59(3),
322–337 (2020)
36. Rybakov, K.A.: Spectral method of analysis and optimal estimation in linear stochastic systems.
Int. J. Model. Simul. Sci. Comput. 11(3), 2050022 (2020)
37. Semenov, A.S.: Essentials of fractal programming. In: Jain, L.C., Favorskaya, M.N., Nikitin,
I.S., Reviznikov, D.L. (eds.) Advances in Theory and Practice of Computational Mechanics:
Proceedings of the 21st International Conference on Computational Mechanics and Modern
Applied Software Systems, SIST, vol. 173, pp. 373–386, Springer, Singapore (2020)
38. Semenov, A.S.: Prototype based programming with fractal algebra. AIP Conf. Proc. 2181,
020009 (2019)
39. Source codes Google-scripts. https://github.com/LevChern/eduprocess. Last accessed 4 Aug
2020
9
28. Panteleev, A.V., Pis’mennaya, V.A.: Application of a memetic algorithm for the optimal control
of bunches of trajectories of nonlinear deterministic systems with incomplete feedback. J.
Comput. Syst. Sci. Int. 57(1), 25–36 (2018)
29. Chugai, K.N., Kosachev, I.M., Rybakov, K.A.: Approximate Filtering Methods in ContinuousTime Stochastic Systems. In: Jain, L.C., Favorskaya, M.N., Nikitin, I.S., Reviznikov, D.L.
(eds.) Advances in Computational Mechanics and Numerical Simulation. Smart Innovation,
Systems and Technologies, vol. 173, pp. 351–371. Springer, Singapore (2020)
30. Averina, T.A., Rybakov, K.A.: Using maximum cross section method for filtering jumpdiffusion random processes. Rus. J. Numer. Anal. Math. Model.ing 35(2), 55–67 (2020)
31. Kuzmina, N.M., Ridley, A.N.: A method for estimating the impact of the fare rules conditions.
Civ. Aviat. High Technol. 224(2), 138–146 (2016)
32. Bochkov, A.V., Zhigirev, N.N., Ridley, A.N.: Method of recovery of priority vector for
alternatives under uncertainty or incomplete expert assessment. Dependability 17(3), 41–48
(2017)
33. Morozov, AYu., Reviznikov, D.L.: Adaptive interpolation algorithm based on a kd-tree
for numerical integration of systems of ordinary differential equations with interval initial
conditions. Differ. Eqn. 54(7), 945–956 (2018)
34. Morozov, AYu., Reviznikov, D.L., Gidaspov, VYu.: Adaptive interpolation algorithm based
on a kd-tree for the problems of chemical kinetics with interval parameters. Math. Models
Comput. Simul. 11(4), 622–633 (2019)
35. Rybakov, K.A.: Modeling and analysis of output processes of linear continuous stochastic
systems based on orthogonal expansions of random functions. J. Comput. Sys. Sc. Int. 59(3),
322–337 (2020)
36. Rybakov, K.A.: Spectral method of analysis and optimal estimation in linear stochastic systems.
Int. J. Model. Simul. Sci. Comput. 11(3), 2050022 (2020)
37. Semenov, A.S.: Essentials of fractal programming. In: Jain, L.C., Favorskaya, M.N., Nikitin,
I.S., Reviznikov, D.L. (eds.) Advances in Theory and Practice of Computational Mechanics:
Proceedings of the 21st International Conference on Computational Mechanics and Modern
Applied Software Systems, SIST, vol. 173, pp. 373–386, Springer, Singapore (2020)
38. Semenov, A.S.: Prototype based programming with fractal algebra. AIP Conf. Proc. 2181,
020009 (2019)
39. Source codes Google-scripts. https://github.com/LevChern/eduprocess. Last accessed 4 Aug
2020