Exploration of artificial neural network to predict morphology of TiO2 nanotube

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.

[1]  Sankar K. Pal,et al.  Neurocomputing: Motivations, Models, and Hybridization - Guest Editors' Introduction , 1996, Computer.

[2]  Craig A. Grimes,et al.  A review on highly ordered, vertically oriented TiO2 nanotube arrays: Fabrication, material properties, and solar energy applications , 2006 .

[3]  K. T. Chau,et al.  A new battery available capacity indicator for electric vehicles using neural network , 2002 .

[4]  Thirumalai Parthiban,et al.  Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells , 2007 .

[5]  Kouji Yasuda,et al.  Mechanistic Aspects of the Self-Organization Process for Oxide Nanotube Formation on Valve Metals , 2007 .

[6]  Yiming Zhang,et al.  Revisiting Hume-Rothery’s Rules with artificial neural networks , 2008 .

[7]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[8]  Kouji Yasuda,et al.  TiO2 nanotubes: Self-organized electrochemical formation, properties and applications , 2007 .

[9]  Jiahua Zhu,et al.  Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced polytetrafluoroethylene composites with artificial neural networks , 2009 .

[10]  G. Thompson,et al.  Influence of water content on nanotubular anodic titania formed in fluoride/glycerol electrolytes , 2009 .

[11]  S.K. Pal,et al.  Neurocomputing motivation, models, and hybridization , 1996, Computer.

[12]  Sofiane Guessasma,et al.  Microstructure of APS alumina–titania coatings analysed using artificial neural network , 2004 .

[13]  M. Vázquez,et al.  Influence of Anodic Conditions on Self-ordered Growth of Highly Aligned Titanium Oxide Nanopores , 2007, Nanoscale Research Letters.

[14]  Craig A. Grimes,et al.  A new benchmark for TiO2 nanotube array growth by anodization , 2007 .

[15]  Craig A. Grimes,et al.  Extreme Changes in the Electrical Resistance of Titania Nanotubes with Hydrogen Exposure , 2003 .

[16]  Synthesis of nano-SnO2 and neural network simulation of its photocatalytic properties , 2010 .

[17]  Siddhartha Datta,et al.  Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO2 surface , 2010, Expert Syst. Appl..

[18]  Young-Jig Kim,et al.  Synthesis of effective titania nanotubes for wastewater purification , 2008 .

[19]  Mohammadreza Khanmohammadi,et al.  A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples , 2010 .