Recursive Estimation of Dynamic Modular RBF Networks
Abstract
In this paper, recursive estimation algorithms for dynamic modular networks are developed. The models are based on Gaussian RBF networks and the gating network is considered in two stages: At first, it is simply a time-varying scalar and in the second, it is based on the state, as in the mixture of local experts scheme. The resulting algorithm uses Kalman filter estimation for the model estimation and the gating probability estimation. Both, 'hard' and 'soft' competition based estimation schemes are developed where in the former, the most probable network is adapted and in the latter all networks are adapted by appropriate weighting of the data.
Cite
Text
Kadirkamanathan and Kadirkamanathan. "Recursive Estimation of Dynamic Modular RBF Networks." Neural Information Processing Systems, 1995.Markdown
[Kadirkamanathan and Kadirkamanathan. "Recursive Estimation of Dynamic Modular RBF Networks." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/kadirkamanathan1995neurips-recursive/)BibTeX
@inproceedings{kadirkamanathan1995neurips-recursive,
title = {{Recursive Estimation of Dynamic Modular RBF Networks}},
author = {Kadirkamanathan, Visakan and Kadirkamanathan, Maha},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {239-245},
url = {https://mlanthology.org/neurips/1995/kadirkamanathan1995neurips-recursive/}
}