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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

Analytical and Numerical Study of the Selective Properties of the Errors Unbiasedness Criterion in the Problems of Inductive Modeling

Volumen 44, Edición 4, 2012, pp. 1-12
DOI: 10.1615/JAutomatInfScien.v44.i4.10
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SINOPSIS

The selective properties of the error unbiasedness criterion and its relationship to the known GMDH criteria, including the classic solutions unbiasedness criterion, are studied. Theoretically, it is proved that the errors unbiasedness criterion is an adequate external GMDH criterion. The behavior of the minimum of this criterion for different levels of noise in the data was investigated numerically and it was shown that it possessed the noise immunity property.

REFERENCIAS
  1. Ivakhnenko A.G., Stepashko V.S. , Simulation noise immunity.

  2. Ivakhnenko A.G., Savchenko Е.А., Ivakhnenko G.A. , The GMDH algorithm for choosing the optimal model by the external criterion of the error with the definition extension by the model bias and its use in committees and neural networks.

  3. Ivakhnenko A.G., Ivakhnenko G.A., Savchenko E.A., GMDH algorithm for optimal model choice by the external error criterion with the definition extension by model bias and its applications to the committees and neural networks.

  4. Ivakhnenko A.G., Savchenko E.A., Investigation of efficiency of additional determination method of the model selection in the modeling problems by application of GMDH algorithm.

  5. Savchenko E.A., Stepashko V.S. , The analysis of the selective properties of GMDH criteria while their successive application.

  6. Savchenko E.A., Stepashko V.S., Semina L.P. , Numerical study of the selective properties of the criterion of the errors unbiasedness.

  7. Stepashko V.S. , GMDH algorithms as a basis for automation of the modeling process by experimental data.

  8. Stepashko V.S. , Method of critical variances as analytical tool of theory of inductive modeling.

  9. Stepashko V.S. , Structural identification of predicting models in a planned experiment.

CITADO POR
  1. Savchenko E., The Technology for Solving the Problem of Modeling and Forecasting Based on Inductive Approach, Kibernetika i vyčislitelʹnaâ tehnika, 2015, 180, 2015. Crossref

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