Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach
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...
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th-cmuir.6653943832-73532014-08-30T04:00:52Z Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach Laosiritaworn W. Ngamjarurojana A. Yimnirun R. Laosiritaworn Y. 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. 2014-08-30T04:00:52Z 2014-08-30T04:00:52Z 2010 Conference Paper 150193 10.1080/00150191003677064 83787 FEROA http://www.scopus.com/inward/record.url?eid=2-s2.0-79955697215&partnerID=40&md5=5f3d18dbbca77065a0f3f98985333551 http://cmuir.cmu.ac.th/handle/6653943832/7353 English |
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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. |
format |
Conference or Workshop Item |
author |
Laosiritaworn W. Ngamjarurojana A. Yimnirun R. Laosiritaworn Y. |
spellingShingle |
Laosiritaworn W. Ngamjarurojana A. Yimnirun R. Laosiritaworn Y. Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
author_facet |
Laosiritaworn W. Ngamjarurojana A. Yimnirun R. Laosiritaworn Y. |
author_sort |
Laosiritaworn W. |
title |
Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
title_short |
Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
title_full |
Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
title_fullStr |
Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
title_full_unstemmed |
Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach |
title_sort |
modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: artificial neural network approach |
publishDate |
2014 |
url |
http://www.scopus.com/inward/record.url?eid=2-s2.0-79955697215&partnerID=40&md5=5f3d18dbbca77065a0f3f98985333551 http://cmuir.cmu.ac.th/handle/6653943832/7353 |
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