Noises generated by nonlinear autoregression | Zendy

Kumičák J. | Zendy

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Noises generated by nonlinear autoregression

Author(s) -

Kumičák J.

Publication year - 1994

Publication title -

annalen der physik

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.009

H-Index - 68

eISSN - 1521-3889

pISSN - 0003-3804

DOI - 10.1002/andp.19945060307

Subject(s) - autoregressive model , generalization , white noise , nonlinear system , noise (video) , mathematics , stability (learning theory) , function (biology) , constant (computer programming) , computer science , algorithm , statistical physics , mathematical analysis , physics , econometrics , artificial intelligence , telecommunications , quantum mechanics , machine learning , evolutionary biology , image (mathematics) , biology , programming language

A nonlinear generalization of autoregressive scheme of first order is suggested as approximate model for 1/ f k noises. The iterative generation makes use of reducing function instead of a constant. Computer simulations – carried out over three decades of frequency – have demonstrated that there is such a family of these functions that to any function of the family there exists a unique value of standard deviation of white noise source such that the noise generated by the iterative scheme has the spectral factor k ≈ 1. Implications of the results for understanding the origin, structural stability and ubiquity of 1/f noise are discussed.

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