Literature DB >> 16383522

Detecting nonlinearity in structural systems using the transfer entropy.

J M Nichols1, M Seaver, S T Trickey, M D Todd, C Olson, L Overbey.   

Abstract

The transfer entropy was recently proposed as a means of exploring coupling in dynamical systems. Transfer entropy is an information theoretic that quantifies the degree to which one dynamical process affects the transition probabilities (dynamics) of another. Here we demonstrate how this metric may be utilized to detect the presence of nonlinearity in a system. Using the method of surrogate data, the transfer entropy computed at various lag times are compared to values computed from linearized surrogates. The transfer entropy is shown to be a more sensitive indicator of nonlinearity than is the mutual information for both simulated and experimental data. This technique is particularly applicable to the field of structural health monitoring, where damage is often equated with the presence of a nonlinearity in an otherwise linear system.

Year:  2005        PMID: 16383522     DOI: 10.1103/PhysRevE.72.046217

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  5 in total

1.  Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network.

Authors:  Wagner Endo; Fernando P Santos; David Simpson; Carlos D Maciel; Philip L Newland
Journal:  J Comput Neurosci       Date:  2015-02-03       Impact factor: 1.621

2.  Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.

Authors:  Shinya Ito; Michael E Hansen; Randy Heiland; Andrew Lumsdaine; Alan M Litke; John M Beggs
Journal:  PLoS One       Date:  2011-11-15       Impact factor: 3.240

3.  Quantifying 'causality' in complex systems: understanding transfer entropy.

Authors:  Fatimah Abdul Razak; Henrik Jeldtoft Jensen
Journal:  PLoS One       Date:  2014-06-23       Impact factor: 3.240

4.  Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy.

Authors:  Zhe Wu; Qiang Zhang; Lixin Wang; Lifeng Cheng; Jingbo Zhou
Journal:  Entropy (Basel)       Date:  2018-11-13       Impact factor: 2.524

5.  Measuring information-transfer delays.

Authors:  Michael Wibral; Nicolae Pampu; Viola Priesemann; Felix Siebenhühner; Hannes Seiwert; Michael Lindner; Joseph T Lizier; Raul Vicente
Journal:  PLoS One       Date:  2013-02-28       Impact factor: 3.240

  5 in total

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