An interpretable and converging set-membership algorithm

Authors:
M. Nayeri;M. S. Liu;J. R. Deller
Affiliations:
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA;Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA;Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
Venue:
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Year:
1993

Citing 2
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Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise

IEEE Transactions on Information Theory
System identification

System identification

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Abstract

Set membership (SM)-based techniques, with least square error overlay, suffer from a trade-off between interpretability and proof of convergence. The authors introduce a modified SM algorithm with 'forgetting' covariance updating in conjunction with minimum volume data selecting strategy. The convergence properties of this algorithm and its resemblance to the stochastic approximation method are discussed.