Title

On-Line System Identification Using Context Discernment

Published In

Proceedings of the International Joint Conference on Neural Networks

Document Type

Citation

Publication Date

12-1-2005

Abstract

Mathematical models are often used in system identification applications. The dynamics of most systems, however, change over time and the sources of these changes cannot always be directly determined or measured. To maintain model accuracy, it is desirable to design system identifiers that can adapt to these dynamical shifts. We use reinforcement learning to train an agent to recognize dynamical changes in a modeled system and to estimate new parameter values for the model. The subsequent actions of this agent are characterized as "moving" the parameterized model on an optimal trajectory in model parameter space. It is found that this method is capable of quickly and accurately discerning the correct parameter values. © 2005 IEEE.

Locate the Document

https://doi.org/10.1109/IJCNN.2005.1555953

DOI

10.1109/IJCNN.2005.1555953

Persistent Identifier

https://archives.pdx.edu/ds/psu/37318

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