Networks with Learned Unit Response Functions
Abstract
Feedforward networks composed of units which compute a sigmoidal func(cid:173) tion of a weighted sum of their inputs have been much investigated. We tested the approximation and estimation capabilities of networks using functions more complex than sigmoids. Three classes of functions were tested: polynomials, rational functions, and flexible Fourier series. Un(cid:173) like sigmoids, these classes can fit non-monotonic functions. They were compared on three problems: prediction of Boston housing prices, the sunspot count, and robot arm inverse dynamics. The complex units at(cid:173) tained clearly superior performance on the robot arm problem, which is a highly non-monotonic, pure approximation problem. On the noisy and only mildly nonlinear Boston housing and sunspot problems, differences among the complex units were revealed; polynomials did poorly, whereas rationals and flexible Fourier series were comparable to sigmoids.
Cite
Text
Moody and Yarvin. "Networks with Learned Unit Response Functions." Neural Information Processing Systems, 1991.Markdown
[Moody and Yarvin. "Networks with Learned Unit Response Functions." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/moody1991neurips-networks/)BibTeX
@inproceedings{moody1991neurips-networks,
title = {{Networks with Learned Unit Response Functions}},
author = {Moody, John and Yarvin, Norman},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {1048-1055},
url = {https://mlanthology.org/neurips/1991/moody1991neurips-networks/}
}