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

Abstract:

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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.

Info:

Periodical:

Key Engineering Materials (Volumes 336-338)

Edited by:

Wei Pan and Jianghong Gong

Pages:

2497-2500

DOI:

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

Citation:

Q. Luo and Q. L. Ren, "Application of Improved BP Neural Network in the Preparation Processing of the SrTiO3 Nanocrystalline", Key Engineering Materials, Vols. 336-338, pp. 2497-2500, 2007

Online since:

April 2007

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Price:

$38.00

[1] L.C. Jiao: Theory of Neural Net System (Xidian University Press, China 1990).

[2] X.R. Wang and Z.L. Wen: Applied Regression Analysis (Chongqing University Press, China 1991).

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