Disorder identification in hysteresis data: recognition analysis of the random-bond-random-field Ising model.
Phys Rev Lett
; 103(15): 157203, 2009 Oct 09.
Article
in En
| MEDLINE
| ID: mdl-19905664
ABSTRACT
An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond-random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.
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Collection:
01-internacional
Database:
MEDLINE
Type of study:
Clinical_trials
/
Diagnostic_studies
Language:
En
Journal:
Phys Rev Lett
Year:
2009
Document type:
Article
Affiliation country:
United States