[1] N. Guliyev, V. Ismailov, A single hidden layer feedforward network with only one neuron in the hidden layer san approximate any univariate function, Neural Computation, 28 (2016), 1289-1304.

[2] D. Costarelli, R. Spigler, Approximation results for neural network operators activated by sigmoidal functions, Neural Networks, 44 (2013), 101-106.

[3] D. Costarelli, G. Vinti, Pointwise and uniform approximation by multivariate neural network operators of the max-product type, Neural Networks, 81 (2016), 81-90.

[4] D. Costarelli, R. Spigler, Solving numerically nonlinear systems of balance laws by multivariate sigmoidal functions approximation, Computational and Applied Mathematics, 37, No. 1 (2018), 99-133.

[5] D. Costarelli, G. Vinti, Convergence for a family of neural network operators in Orlicz spaces, Mathematische Nachrichten, 290, No. 2-3 (2017), 226-235.

[6] J. Dombi, Z. Gera, The Approximation of Piecewise Linear Membership Functions and Lukasiewicz Operators, Fuzzy Sets and Systems, 154, No. 2 (2005), 275-286.

[7] I. A. Basheer, M. Hajmeer, Artificial Neural Networks: Fundamentals, Computing, Design, and Application, Journal of Microbiological Methods, 43 (2000), 3-31.

[8] Z. Chen, F. Cao, The Approximation Operators with Sigmoidal Functions, Computers & Mathematics with Applications, 58 (2009), 758-765.

[9] Z. Chen, F. Cao, The Construction and Approximation of a Class of Neural Networks Operators with Ramp Functions, Journal of Computational Analysis and Applications, 14 (2012), 101-112.

[10] Z. Chen, F. Cao, J. Hu, Approximation by Network Operators with Logistic Activation Functions, Applied Mathematics and Computation, 256 (2015), 565- 571.

[11] D. Costarelli, R. Spigler, Constructive Approximation by Superposition of Sigmoidal Functions, Anal. Theory Appl., 29 (2013), 169-196.

[12] N. Kyurkchiev, A new class of activation function based on the correcting amendments, Int. J. for Sci., Res. and Developments, 6, No. 2 (2018), 565-568.

[13] N. Kyurkchiev, A. Iliev, S. Markov, Some techniques for recurrence generating of activation functions, LAP LAMBERT Academic Publishing (2017), ISBN: 978-3-330-33143-3.

[14] A. Iliev, N. Kyurkchiev, S. Markov, A Note on the New Activation Function of Gompertz Type, Biomath Communications, 4, No. 2 (2017).

[15] W. Duch, N. Jankowski, Survey of neural transfer functions, Neural Computing Surveys, 2 (1999), 163-212.

[16] F. Hausdorff, Set Theory, 2nd ed., Chelsea Publ., New York (1962).

[17] B. Sendov, Hausdorff Approximations, Kluwer, Boston (1990).

[18] N. I. Akhiezer, Theory of approximation, Nauka, Moscow (1965), 2nd ed., (in Russian), English translation (of the 1st edition): N. I. Achiezer, Theory of approximation, Translated by Charles J. Hyman: Frederick Ungar Publishing, New York (1956).

[19] E. Voronovskaya, Odd polynomials of least deviation, Dokl. AN SSSR, 159, No. 4 (1964), 715-718. (in Russian)

[20] S. Markov, Bl. Sendov, On the numerical evaluation of a class of polynomials of best approximation, Ann. Univ. Sofia, Fac. Math., 61 (1966/67), 17-27. (in Bulgarian).

[21] A. Andreev, N. Kyurkchiev, Approximation of some impulse functions - implementation in programming environment MATHEMATICA, Proceedings of the 43 Spring Conference of the Union of Bulgarian Mathematicians, Borovetz, April 2-6, 2014, 111-117.

[22] N. Kyurkchiev, A. Andreev, Hausdorff approximation of functions different from zero at one point - implementation in programming environment MATHEMATICA, Serdica J. of Computing, 7, No. 2 (2013), 135-142.

[23] N. Kyurkchiev, A. Andreev, Synthesis of slot aerial grids with Hausdorff-type directive patterns - implementation in programming envoronment Mathematica, C. R. Acad. Bulg. Sci., 66, No. 11 (2013), 1521-1528.

[24] N. Kyurkchiev, A. Andreev, Approximation and Antenna and Filters synthesis: Some Moduli in Programming Environment MATHEMATICA, LAP LAMBERT Academic Publishing, Saarbrucken (2014), ISBN: 978-3-659-53322-8.

[25] N. Kyurkchiev, A. Iliev, On the Hausdorff distance between the Heaviside function and some transmuted activation functions, Mathematical Modelling in Engineering Applications, 2, No. 1 (2016), 1-5.

[26] N. Kyurkchiev, S. Markov, Hausdorff approximation of the sign function by a class of parametric activation functions, Biomath Communications, 3, No. 2 (2016).

[27] A. Iliev, N. Kyurkchiev, S. Markov, A Family of Recurrence Generated Parametric Activation Functions with Applications to Neural Networks, International Journal on Research Innovations in Engineering Science and Technology, 2, No. 1 (2017), 60-68.

[28] N. Kyurkchiev, A. Iliev, S. Markov, Families of Recurrence Generated Three and Four Parametric Activation Functions, International Journal for Scientific Research & Development, 5, No. 1 (2017), 746-750.

[29] V. Kyurkchiev, A. Iliev, N. Kyurkchiev, On Some Families of Recurrence Generated Activation Functions, International Journal of Scientific Engineering and Applied Science, 3, No. 3 (2017), 243-248.

[30] N. Kyurkchiev, A. Iliev, A Note On The New Fibonacci Hyperbolic Tangent Activation Function, International Journal of Innovative Science Engineering and Technology, 4, No. 5 (2017), 364-368.

[31] A. Golev, A. Iliev, N. Kyurkchiev, A Note on the Soboleva’ Modified Hyperbolic Tangent Activation Function, Int. J. of Sci., Eng. and Techn., 4, No. 6 (2017), 177-182.

[32] A. Malinova, A. Golev, A. Iliev, N. Kyurkchiev, A Family of Recurrence Generating Activation Functions Based on Gudermann Function, International Journal of Engineering Researches and Management Studies, 4, No. 8 (2017), 38-48.

[33] N. Kyurkchiev, A Note on the Volmer’s Activation (VA) Function, C. R. Acad. Bulg. Sci., 70, No. 6 (2017), 769-776.

[34] V. Kyurkchiev, N. Kyurkchiev, A Family of Recurrence Generated Functions Based on ”Half-Hyperbolic Tangent Activation Function”, Biomedical Statistics and Informatics, 2, No. 3 (2017), 87-94.

[35] N. Kyurkchiev, A. Iliev, A new class of 2-parametric deterministic activation functions, Int. J. of Advanced Research in Computer and Communication Engineering, 7, No. 3 (2018), 481-483.

[36] N. Kyurkchiev, A new class activation functions with application in the theory of impulse technics, Journal of Mathematical Sciences and Modelling, 1, No. 1 (2018), 15-20.

[37] C. Zhang, S. Zhou, B. Chain, Hybrid Epidemics-A Case Study on Computer Worm Conficker, PLoS ONE, 10, No. 5 (2015), e0127478.

[38] P. Porras, H. Saidi, V. Yegneswaran, An Analysis of Conficker’S Logic and Rendezvous Points, SRI international technical report, March 19, (2009).

[39] N. Kyurkchiev, A. Iliev, A. Rahnev, T. Terzieva, A new analysis of Code Red and Witty worms behavior, Communications in Applied Analysis, 23 (2), 2019, 267-285.

[40] N. Kyurkchiev, A. Iliev, A. Rahnev, T. Terzieva, Some New Approaches for Modelling Large-Scale Worm Spreading on the Internet. II, Neural, Parallel, and Scientific Computations, 27 (2019). (to appear).