Application of Improved BP Neural Network in the Preparation Processing of the SrTiO3 Nanocrystalline

Qiang Luo; Qing Li Ren

doi:10.4028/www.scientific.net/KEM.336-338.2497

Paper Titles

Geometric Design of Cycloidal Drives with Ceramic Ball Meshing Elements
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Design of Ceramic Composite Based on the Impact Resistance and its Machining Application
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Parallel Simulation of Ceramic Grain Growth on the Platform of MPI
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Quantitative Evaluation for New Bone Growth of Bone Repairing Biomaterial Based on Color Image Segmentation
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Application of Improved BP Neural Network in the Preparation Processing of the SrTiO₃ Nanocrystalline
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Finite Element Method for Thermal Analysis and Ablative Calculation of Heat Protection Materials
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Lifetime Control and Prediction of ZrO₂/CePO₄ Composites
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First-Principles Study of Nonmetal-Doped Titanium Oxides
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First Principle Study on Electronic Structure of Nanocrystalline BaTiO₃ Ceramics
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HomeKey Engineering MaterialsKey Engineering Materials Vols. 336-338Application of Improved BP Neural Network in the...

Application of Improved BP Neural Network in the Preparation Processing of the SrTiO₃ Nanocrystalline

Abstract:

A three-layer back-propagation neural network model based on the non-linear relationship between the size of the SrTiO3 nanocrystalline and the technology factors, such as reaction time, reaction temperature, raw material adding amount of NaOH and SrCl2, and the rate of TiCl4/Hl, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back-propagation neural network model is very efficient for predication of the SrTiO3 nanocrystalline size.