Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/1482
Title: Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach
Authors: Laosiritaworn W.
Ngamjarurojana A.
Yimnirun R.
Laosiritaworn Y.
Issue Date: 2010
Abstract: In this work, the relationship between hysteresis area of hard lead zirconate titanate and external perturbation was modeled using the Artificial Neural Network (ANN). The model developed has the applied electric field parameters and temperature as inputs, and the hysteresis area as an output. Then ANN was trained with experimental data and used to predict hysteresis area of the unseen testing patterns of input. The predicted and the actual data of the testing set were found to agree very well for all considered input parameters. Furthermore, unlike previous power-law investigation where the low-field data had to be discarded in avoiding non-convergence problem, this work can model the data for the whole range with fine accuracy. This therefore suggests the ANN success in modeling hard ferroelectric hysteresis properties and underlines its superior performance upon typical power-law scaling technique. © Taylor & Francis Group, LLC.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-79955697215&partnerID=40&md5=5f3d18dbbca77065a0f3f98985333551
http://cmuir.cmu.ac.th/handle/6653943832/1482
ISSN: 150193
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.