A new recursive least-squares identification algorithm based on singular value decomposition
Youmin M. Zhang, Qingguo Li, Guanzhong Dai, Hongcai Zhang · 2002
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.>