This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Contr. Signals Syst. 2(1989) 303–314.
Chen, T.P., Chen, H.: Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to a dynamic system. IEEE Trans. Neural Netw. 6(1995) 911–917.
Chen, T.P.: Approximation problems in system identification with neural networks. Science in China, Series A 24(1)(1994) 1–7.
Attali, J.G., Pages, G.: Approximation of functions by a multilayer perceptron: a new approach. Neural Netw. 10(1997) 1069–1081.
Cao, F.L., Xu Z.B., Liang J.Y.: Approximation of ploynomial functions by neural networks: construction of network and algorithm of approximation. Chinese Journal of Computers 26(8)(2003) 906–912.
Xu Z.B., Cao, F.L.: Simultaneous Lp-Approximation order for neural networks. Neural Netw. 18(2005) 914–923.
Timan, A.F.: Theory of approximation of functions of a real variable. New York: Macmillan (1963).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Xu, Z., Jing, J. (2008). Constructive Approximation Method of Polynomial by Neural Networks. In: Wang, R., Shen, E., Gu, F. (eds) Advances in Cognitive Neurodynamics ICCN 2007. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8387-7_177
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8387-7_177
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8386-0
Online ISBN: 978-1-4020-8387-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)