Gain Adaptation Beats Least Squares?
- Andrew Barto, Department of Computer Science, University of Massachusetts at Amherst
- Richard S. Sutton, Department of Computing Science, University of Alberta
I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n^2). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time.
Citation
A. Barto, R. Sutton. "Gain Adaptation Beats Least Squares?". Yale Workshop on Adaptive and Learning Systems, pp 161-166, January 1992.| Keywords: | Kalman filter, least-squares method, machine learning, machine learning |
| Category: | In Workshop |
BibTeX
@misc{Barto+Sutton:YaleWorkshoponAdaptiveandLearningSystems92,
author = {Andrew Barto and Richard S. Sutton},
title = {Gain Adaptation Beats Least Squares?},
Pages = {161-166},
booktitle = {},
year = 1992,
}Last Updated: May 31, 2007Submitted by Staurt H. Johnson
