Three-layer feedforward structures smoothly approximating polynomial functions

Authors:
Yuichi Nakamura;Masahiro Nakagawa
Affiliations:
Anan National College of Technology, Anan, Tokushima, Japan;Nagaoka University of Technology, Nagaoka, Niigata, Japan
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

Citing 7
Cited 0

On the approximate realization of continuous mappings by neural networks

Neural Networks
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Neural Networks
Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Neural Networks
Approximating functions by neural networks: a constructive solution in the uniform norm

Neural Networks
Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results

Neural Networks
Constructive function-approximation by three-layer artificial neural networks

Neural Networks
Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers a structure of three-layer feedforward networks that approximate polynomial functions. The feedforward network has some system parameters such as the coupling coefficients and the biases. The structure of the feedforward network is determined by the system parameters. For any polynomial function, a simple calculation method of the parameters is proposed when the three-layer feedforward network sufficiently approximates the polynomial function. Moreover, it is shown that the obtained feedforward network smoothly approximates the polynomial function.