Abstract
This paper presents a function approximation to a general class of polynomials by using one-hidden-layer feedforward neural networks(FNNs). Both the approximations of algebraic polynomial and trigonometric polynomial functions are discussed in details. For algebraic polynomial functions, an one-hidden-layer FNN with chosen number of hidden-layer nodes and corresponding weights is established by a constructive method to approximate the polynomials to a remarkable high degree of accuracy. For trigonometric functions, an upper bound of approximation is therefore derived by the constructive FNNs. In addition, algorithmic examples are also included to confirm the accuracy performance of the constructive FNNs method. The results show that it improves efficiently the approximations of both algebraic polynomials and trigonometric polynomials. Consequently, the work is really of both theoretical and practical significance in constructing a one-hidden-layer FNNs for approximating the class of polynomials. The work also paves potentially the way for extending the neural networks to approximate a general class of complicated functions both in theory and practice.





Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Cybenko G (1989) Approximation by superpositions of a sigmoid function. Math Contr Signals Syst 2:303–314
Hornik K, Stinchombe M, White H (1989) Multilayer feedforward networks are universal approximator. Neural Netw 2:359–366
Hornik K (1993) Some new results on neural network approximation. Neural Netw 6:1069–1072
Kůrkova V, Kainen PC, Kreinovich V (1997) Estimates for the number of hidden units and variation with respect to half-space. Neural Netw 10:1068–1078
Leshno M, Lin VY, Pinks A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6:861–867
Attali JG, Pages G (1997) Approximation of functions by a multilayer perceptron: a new approach. Neural Netw 10:1069–1081
Chui CK, Li X (1992) Approximation by ridge functions and neural networks with one hidden layer. J Approx Theory 70:131–141
Hornik K, Stinchcombe M, White H, Auer P (1994) Degree of approximation results for feedforward networks approximating unknown mappings and theirs derivatives. Neural Comput 6:1262–1275
Chen TP, Chen H (1995) Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to a dynamic system. IEEE Trans Neural Netw 6:911–917
Chen TP (1994) Approximation problems in system identification with neural networks. Sci China Ser A 24(1):1–7
Yoshifusa I (1991) Approximation of functions on a compact set by finite sums of sigmoid function without scaling. Neural Netw 4:817–826
Chen XH, White H (1999) Improved rates and asymptotic normality for nonparametric neural network estimators. IEEE Trans Inform Theory 45:682–691
Gallant AR, White H (1992) On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Netw 5:129–138
Cao FL, Xu ZB, Liang JY (2003) Approximation of ploynomial functions by neural networks: construction of network and algorithm of approximation. Chin J Comput 26(8):906–912 (in Chinese)
Xu ZB, Cao FL (2005) Simultaneous L p-approximation order for neural networks. Neural Netw 18:914–923
Timan AF (1963) Theory of approximation of functions of a real variable. Macmillan, New York
Suzuki S (1998) Constructive function approximation by three-layer artificial neural networks. Neural Netw 11:1049–1058
Acknowledgement
This work was supported by Natural Science Foundation of China (NOs.11001227, 60972155),the Key Project of Chinese Ministry of Education (No.108176), Natural Science Foundation Project of CQ CSTC(No.CSTC, 2009BB2306, CSTC2009BB2305), the Fundamental Research Funds for the Central Universities(No.XDJK2010B005, XDJK2010C023).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, JJ., Chen, BL. & Yang, CY. Approximation of algebraic and trigonometric polynomials by feedforward neural networks. Neural Comput & Applic 21, 73–80 (2012). https://doi.org/10.1007/s00521-011-0617-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-011-0617-3