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Tracking of uncertain time-varying systems by state-space recursive least-squares with adaptive memory | IEEE Conference Publication | IEEE Xplore

Tracking of uncertain time-varying systems by state-space recursive least-squares with adaptive memory


Abstract:

State-space recursive least-squares (SSRLS) allows the designer to choose an appropriate model, resulting in superior tracking performance over the standard recursive lea...Show More

Abstract:

State-space recursive least-squares (SSRLS) allows the designer to choose an appropriate model, resulting in superior tracking performance over the standard recursive least-squares (RLS) and least mean square (LMS). However, the tracking capability of this algorithm is dependent on the forgetting factor in presence of factors like model uncertainties and time-varying nature of observation noise etc. We address such problems In this work by developing time-varying SSRLS with adaptive memory. The tuning of the forgetting factor is done by stochastic gradient method. The ability to handle time-varying linear systems is a major enhancement of our previous work. The new filter is therefore, much more flexible and powerful. Based on this theory, we design a tracking algorithm that efficiently tracks time-varying systems.
Date of Conference: 04-04 September 2004
Date Added to IEEE Xplore: 31 January 2005
Print ISBN:0-7803-8635-3
Print ISSN: 2158-9860
Conference Location: Taipei, Taiwan

References

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