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ISNN
2004
Springer

Geometric Interpretation of Nonlinear Approximation Capability for Feedforward Neural Networks

14 years 4 months ago
Geometric Interpretation of Nonlinear Approximation Capability for Feedforward Neural Networks
This paper presents a preliminary study on the nonlinear approximation capability of feedforward neural networks (FNNs) via a geometric approach. Three simplest FNNs with at most four free parameters are defined and investigated. By approximations on one-dimensional functions, we observe that the Chebyshev-polynomials, Gaussian, and sigmoidal FNNs are ranked in order of providing more varieties of nonlinearities. If neglecting the compactness feature inherited by Gaussian neural networks, we consider that the Chebyshev-polynomial-based neural networks will be the best among three types of FNNs in an efficient use of free parameters.
Bao-Gang Hu, Hong-Jie Xing, Yujiu Yang
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ISNN
Authors Bao-Gang Hu, Hong-Jie Xing, Yujiu Yang
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