Exploration of artificial neural network to predict morphology of TiO2 nanotube

Authors:
Hongyi Zhang;Jianling Zhao;Yuying Jia;Xuewen Xu;Cencun Tang;Yangxian Li
Affiliations:
School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China and Key Laboratory of Semiconductor Materials Science, Institute of Semiconductors, Chinese Academ ...;School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China;School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China;School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China;School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China;School of Material Science and Engineering, Hebei University of Technology, Tianjin 300130, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Artificial neural network (ANN) was developed to predict the morphology of TiO"2 nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO"2 nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO"2 nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO"2nanotube fabrication process.