In this paper, a hybrid recursive least squares (HRLS) algorithm for online identification using sequential chunk-by-chunk observations is proposed. By employing the optimization-based least squares (O-LS), the HRLS can be initialized with any chunk of data samples and works successively in two recursive procedures for updating the inverse matrix with minimal dimension and least rank-deficiency, and thereby contributing to fast and stable online identification. Since norms of the output weight and training errors are minimized simultaneously, the HRLS achieves high accuracy in terms of both generalization and approximation. Simulation studies and comprehensive comparisons demonstrate that the HRLS is numerically more stable and superior to other algorithms in terms of accuracy and speed.
Wang, N., Sun, J. C., Er, M. J., & Liu, Y. C. (2016). Hybrid recursive least squares algorithm for online sequential identification using data chunks. Neurocomputing, 174, 651–660. https://doi.org/10.1016/j.neucom.2015.09.090