A new recursive least-squares identification algorithm based on singular value decomposition

Youmin Zhang Youmin Zhang,Hongcai Zhang Hongcai Zhang,Guanzhong Dai Guanzhong Dai,Qingguo Li Qingguo Li

doi:10.1109/cdc.1994.411186

Youmin Zhang Youmin Zhang, Hongcai Zhang Hongcai Zhang + Show 2 more

https://doi.org/10.1109/cdc.1994.411186

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Publication Date: Dec 14, 1994

Citations: 7

Affiliation: Northwestern Polytechnical University

Abstract

Based on singular value decomposition (SVD), a new recursive least-squares identification method, which takes in account input excitation, is proposed in this paper. It is demonstrated that the SVD-based approach proposed in this paper can not only obviously improve the convergence rate, numerical stability of RLS, but also provide much more precise identification results and greatly enhance the robustness of the system identification. Moreover, this algorithm is formulated in the form of vector-matrix and matrix-matrix operations, so it is also useful for parallel computers. >

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