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 …

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 …