Sequential Adaptation of Radial Basis Function Neural Networks and Its Application to Time-Series Prediction
Abstract
We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of succes(cid:173) sive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L 2-norm sense. The RBF network with the F-Projections adaptation algorithm was used for pre(cid:173) dicting a chaotic time-series. We compare its performance to an adapta(cid:173) tion scheme based on the method of stochastic approximation, and show that the F-Projections algorithm converges to the underlying model much faster.
Cite
Text
Kadirkamanathan et al. "Sequential Adaptation of Radial Basis Function Neural Networks and Its Application to Time-Series Prediction." Neural Information Processing Systems, 1990.Markdown
[Kadirkamanathan et al. "Sequential Adaptation of Radial Basis Function Neural Networks and Its Application to Time-Series Prediction." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/kadirkamanathan1990neurips-sequential/)BibTeX
@inproceedings{kadirkamanathan1990neurips-sequential,
title = {{Sequential Adaptation of Radial Basis Function Neural Networks and Its Application to Time-Series Prediction}},
author = {Kadirkamanathan, V. and Niranjan, M. and Fallside, F.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {721-727},
url = {https://mlanthology.org/neurips/1990/kadirkamanathan1990neurips-sequential/}
}