[1]
Barron, A. R. (1933) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inform. Theory
39
(3), 930–945.

[2]
Broyden, C. G. (1965) A class of methods for solving nonlinear simultaneous equations. Math. Comput.
19
(92), 577–593.

[3]
Broyden, C. G., Dennis, J. E. & More, J. J. (1993) On the local and the superlineal convergences of quasi-Newton methods. J. Inst. Math. Appl.
12
(3), 223–246.

[4]
Burden, R. L., Faires, J. D. & Burden, A. M. (2015) Numerical Analysis, ISBN 978-1305253667, Brooks Cole, Boston, USA.

[5]
Cao, F. & Chen, Z. (2009) The approximation operators with sigmoidal functions. Comput. Math. Appl.
58
(4), 758–765.

[6]
Cao, F. & Chen, Z. (2012) The construction and approximation of a class of neural networks operators with ramp functions. J. Comput. Anal. Appl.
14
(1), 101–112.

[7]
Costarelli, D. (2015) Neural network operators: Constructive interpolation of multivariate functions.
Neural Netw.
67, 28–36.

[8]
Costarelli, D. & Vinti, G. (2016) Max-product neural network and quasi interpolation operators activated by sigmoidal functions.
J. Approx. Theory
209, 1–22.

[9]
Costarelli, D. & Vinti, G. (2016) Pointwise and uniform approximation by multivariate neural network operators of the max-product type.
Neural Netw.
81, 81–90.

[10]
Costarelli, D. & Vinti, G. (2016) Approximation by max-product neural network operators of Kantorovich type. Results Math.
69
(3), 505–519.

[11]
Dennis, J. E. & Wolkowicz, H. (1993) Least change secant method, sizing and shifting. SIAM J. Numer. Anal.
30
(5), 1291–1314.

[12]
El-Emary, I. M. M. & El-Kareem, M. M. A. (2008) Towards using genetic algorithms for solving nonlinear equation systems. World Appl. Sci. J.
5
(3), 282–289.

[13]
Goulianas, K., Margaris, A. & Adamopoulos, M. (2013) Finding all real roots of 3 × 3 nonlinear algebraic systems using neural networks. Appl. Math. Comput.
219
(9), 4444–4464.

[14]
Grosan, C., Abraham, A. & Snasel, V. (2012) Solving polynomial systems using a modified line search approach. Int. J. Innovative Comput. Inform. Control
8
(1) (B), 501–526.

[15]
Grosan, C. & Abraham, A. (2012) Multiple solutions of a system of nonlinear equations. Int. J. Innovative Comput. Inform. Control
4
(9), 2161–2170.

[16]
Hahm, N. & Hong, B. I. (2016) A Note on neural network approximation with a sigmoidal function. Appl. Math. Sci.
10
(42), 2075–2085.

[17]
Iliev, A., Kyurkchiev, N. & Markov, S. (2015) On the approximation of the cut and step functions by logistic and Gompertz functions. BIOMATH
4
(2), 1–12.

[18]
Karr, C. L, Weck, B. & Freeman, L. M. (1998) Solution to systems of nonlinear equations via a generic algorithm.
Eng. Appl. Artif. Intell.
11, 369–375.

[19]
Ko, T. H., Sakkalis, T. & Patrikalakis, N. M. (2004) Nonlinear polynomial systems: Multiple roots and their multiplicities. In: Giannini, F. & Pasko, A. (editors), *Proceedings of Shape Modelling International Conference, SMI 2004*, Genova, Italy.

[20]
Mamat, M., Muhammad, K. & Waziri, M. Y. (2014) Trapezoidal Broyden's method for solving systems of nonlinear equations. Appl. Math. Sci.
8
(6), 251–260.

[21]
Manocha, D. (1994) Solving Systems of polynomial equations. IEEE Comput. Graph. Appl.
14
(2), 46–55.

[22]
Margaris, A. & Goulianas, K. (2012) Finding all roots of 2 × 2 non linear algebraic systems using back propagation neural networks. In: Neural Computing and Applications, Vol. 21(5), Springer-Verlag London Limited, pp. 891–904.

[23]
Martinez, J. M. (1994) Algorithms for solving nonlinear systems of equations. In: Continuous Optimization: The State of the Art, Kluwer, pp. 81–108.

[24]
Martynyuk, A. A. (2008) An exploration of polydynamics of nonlinear equations on time scales. ICIC Exp. Lett.
2
(2), 155–160.

[25]
Mathia, K. & Saeks, R. (1995) Solving nonlinear equations using reccurent neural networks. In: *World Congress on Neural Networks (WCNN'95)*, Washington DC, pages I-76 to I-80.

[26]
Mhetre, P. (2012) Genetic algorithm for linear and nonlinear equation. Int. J. Adv. Eng. Technol.
3
(2), 114–118.

[27]
Mishra, D. & Kalra, P. (2007) Modified hopfield neural network approach for solving nonlinear algebraic equations. Eng. Lett.
14
(1), 135–142.

[28]
Nguyen, T. T. (1993) Neural network architecture of solving nonlinear equation systems. Electron. Lett.
29
(16), 1403–1405.

[29]
Oliveira, H. A. & Petraglia, A. (2013) Solving nonlinear systems of functional equations with fuzzy adaptive simulated annealing. Appl. Soft Comput.
13
(11), 4349–4357.

[30]
Ortega, J. M. & Rheinboldt, W. C. (1970) Iterative Solution of Nonlinear Equations in Several Variables, Academic Press, New York.

[31]
Rheinboldt, W. C. (1974) Methods for solving systems of equations. Reg. Conf. Ser. Appl. Math
14.

[32]
Schmidhuber, J. (2015) Deep learning in neural networks: An overview. Neural Netw.
61, 85–117.

[33]
Spedicato, E. & Huang, Z. (1997) Numerical experience with Newton-like methods for nonlinear algebraic systems. Computing
58
(1), 69–89.

[34]
Zhou, G. & Zhu, P. (2010) Iterative algorithms for solving repeated root of nonlinear equation by modified Adomian decomposition method. ICIC Exp. Lett.
4
(2), 595–600.

[35]
Zhoug, X., Zhang, T. & Shi, Y. (2009) Oscillation and nonoscillation of neutral difference equation, with positive and negative coefficients. Int. J. Innovative Comput. Inform. Control
5
(5), 1329–1342.