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Inverse updated systolic RLS algorithm with regularized exponential forgetting. (English) Zbl 0870.93051
This paper investigates the implementation of the idea of block-regularized exponential forgetting in the systolic algorithm for the recursive least squares (RLS) identification. It combines the inverse updated systolic RLS algorithm with covariance update and the block-accumulated regularized exponential forgetting. The regularized forgetting prevents the covariance matrix from unlimited growth and then increases numerical stability of the algorithm for weakly informative data. It is shown that the proposed implementation of the block-regularized forgetting preserves compactness of the systolic estimator with exponential forgetting since the algorithm uses only the connections between the neighboring processors.
Reviewer: K.Uosaki (Tottori)
MSC:
93E24 Least squares and related methods for stochastic control systems
93E12 Identification in stochastic control theory
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References:
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