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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.