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An interpretable and converging set-membership algorithm
By: Deller, J.R.; Liu, M.S.; Nayeri, M.;
1993 / IEEE / 0-7803-0946-4
This item was taken from the IEEE Periodical ' An interpretable and converging set-membership algorithm ' 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.<
Stochastic Approximation Method
Least Square Error Overlay
Minimum Volume Data Selecting Strategy
Signal Processing Algorithms
Adaptive Signal Processing
Least Squares Methods
Least Squares Approximations
Proof Of Convergence