Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volumn 80, Issue 4, 2009, Pages

  Molkov, Ya I ^a   Mukhin, D N ^a   Loskutov, E M ^a   Feigin, A M ^a   Fidelin, G A ^b  

^a INSTITUTE OF APPLIED PHYSICS   (Russian Federation)

^b LOBACHEVSKY STATE UNIVERSITY OF NIZHNY NOVGOROD   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATIVE APPROACH; ARTIFICIAL NEURAL NETWORK; CORRELATION INTEGRAL; DYNAMIC SYSTEMS; EMBEDDING DIMENSIONS; FALSE NEAREST NEIGHBOR; GLOBAL MODELS; LOW NOISE LEVELS; MINIMUM DESCRIPTION LENGTH PRINCIPLE;

DYNAMIC PROGRAMMING; DYNAMICAL SYSTEMS; NEURAL NETWORKS; TIME SERIES;

STOCHASTIC MODELS;

EID: 70350521612     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.80.046207     Document Type: Article

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20. The problem of choice of an optimal time lag for phase variables reconstruction was discussed in many works (see, for instance, Refs.). We use time lag corresponding to the minimum of mutual information function (Ref..
21. Gaussian white noise is used; hereinafter the noise level is defined as ratio of noise and data rms.