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Journal of Automation and Information Sciences

Publicado 12 números por año

ISSN Imprimir: 1064-2315

ISSN En Línea: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

Indexed in

Asymptotics of Linear Recurrent Regression under Diffuse Initialization

Volumen 41, Edición 5, 2009, pp. 41-51
DOI: 10.1615/JAutomatInfScien.v41.i5.50
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SINOPSIS

The behavior of recurrent least squares method in the absence of a priori information with respect to estimated unknown parameters is studied. The developed approach allows one to present its characteristics in analytical form, to explain the divergence phenomenon and to suggest a limiting recurrent estimation algorithm independent of a large parameter characterizing initial uncertainty and leading to divergence.

CITADO POR
  1. Skorohod B. A., Learning Algorithms for Neural Networks and Neuro-Fuzzy Systems with Separable Structures, Cybernetics and Systems Analysis, 51, 2, 2015. Crossref

  2. References, in Diffuse Algorithms for Neural and Neuro-Fuzzy Networks, 2017. Crossref

  3. Skorohod B., RLS Algorithms with Nonlinear Constraints and their Application to Reducing Estimates Fluctuations, 2019 International Russian Automation Conference (RusAutoCon), 2019. Crossref

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