Least squares matrix algorithm for state-space modelling of dynamic systems

Juuso T. Olkkonen (Corresponding Author), Hannu Olkkonen

    Research output: Contribution to journalArticleScientificpeer-review


    This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.
    Original languageEnglish
    Pages (from-to)287-291
    JournalJournal of Signal and Information Processing
    Issue number4
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed


    • State-Space Modelling
    • Dynamic System Analysis
    • EEG


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