Please use this identifier to cite or link to this item:
|Title:||Artificial neural network modeling of ferroelectric hysteresis: an application to soft lead zirconate titanate ceramics|
|Abstract:||In this work, the Artificial Neural Network (ANN) was used to model ferroelectric hysteresis using data measured from soft lead zirconate titanate [Pb (Zr1-xTix)O3 or PZT] ceramics as an application. Data from experiments were split into training, testing and validation dataset. Four ANN models were developed separately to predict output of the hysteresis area, remnant, coercivity and squareness. Each model has two neurons in the input layer, which represent field amplitude and field frequency. The ANNs were trained with varying number of hidden layer and number of neurons in each layer to find the best network architecture with highest accuracy. After the networks have been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the testing data were found to match very well which suggests the ANN success in modeling ferroelectric hysteresis properties obtained from experiments. © (2010) Trans Tech Publications.|
|Appears in Collections:||ENG: Journal Articles|
Files in This Item:
There are no files associated with this item.
Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.