TY - JOUR
T1 - Disorder identification in hysteresis data
T2 - Recognition analysis of the random-bond-random-field ising model
AU - Ovchinnikov, O. S.
AU - Jesse, S.
AU - Bintacchit, P.
AU - Trolier-Mckinstry, S.
AU - Kalinin, S. V.
PY - 2009/10/9
Y1 - 2009/10/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70349881648&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.103.157203
DO - 10.1103/PhysRevLett.103.157203
M3 - Article
AN - SCOPUS:70349881648
SN - 0031-9007
VL - 103
JO - Physical Review Letters
JF - Physical Review Letters
IS - 15
M1 - 157203
ER -