@inproceedings{3084f208758b48f8b6fe645dc2e07dee,
title = "Fourier series neural networks for regression",
abstract = "An innovative efficient and fast neural networks in which hidden neurons are constructed based on Fourier series expansions (FSNN), half-range cosine (FCSNN) and sine expansions (FSSNN) are proposed and tested for linear and nonlinear regulation problems. The results of numerical examples using FSNN are compared with those obtained from traditional linear regression (LP), nonlinear regression (NLP), backward propagation neural networks (BPANN) and radial basis function neural networks (RBFNN). The results obtained from FSNN agree well with those obtained from LP, NLP, BPANN and RBFNN and show global approximation features to the fitting data. Only a few hidden neurons are required to obtain very good and fast convergence of regression as compared with BPANN and RBFNN.",
author = "Wang, {Yung Ming} and Huang, {Li Jeng}",
year = "2018",
month = jun,
day = "22",
doi = "10.1109/ICASI.2018.8394358",
language = "English",
series = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "716--719",
editor = "Lam, {Artde Donald Kin-Tak} and Prior, {Stephen D.} and Teen-Hang Meen",
booktitle = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",
address = "United States",
note = "4th IEEE International Conference on Applied System Innovation, ICASI 2018 ; Conference date: 13-04-2018 Through 17-04-2018",
}