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dc.contributor.authorWimalin Laosiritawornen_US
dc.contributor.authorNatthapong Wongdamnernen_US
dc.contributor.authorRattikorn Yimnirunen_US
dc.contributor.authorYongyut Laosiritawornen_US
dc.description.abstractThis paper proposed an application of Artificial Neural Network (ANN) to concurrently model ferroelectric hysteresis properties of Barium Titanate in both single-crystal and bulk-ceramics forms. In the ANN modeling, there are 3 inputs, which are type of materials (single or bulk), field amplitude and frequency, and 1 output, which is hysteresis area. Appropriate number of hidden layer and hidden node were achieved through a search of up to 2 layers and 30 neurons in each layer. After ANN had been properly trained, a network with highest accuracy was selected. Query file of unseen input data was then input to the selected network to obtain the predicted hysteresis area. From the results, the target and predicted data were found to match very well. This therefore suggests that ANN can be successfully used to concurrently model ferroelectric hysteresis property even though the considered ferroelectrics are with different domains, grains and microscopic crystal structures. Copyright © Taylor &Francis Group, LLC.en_US
dc.subjectMaterials Scienceen_US
dc.subjectPhysics and Astronomyen_US
dc.titleConcurrent artificial neural network modeling of single-crystal and bulk-ceramics ferroelectric-hysteresis: An application to barium titanateen_US
article.volume414en_US Mai Universityen_US University of Technologyen_US on Higher Educationen_US
Appears in Collections:CMUL: Journal Articles

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