Application of wavelet neural network in cortisol solubility

被引：0

作者：

Chen, Jianxin ^{[1
]}

Shi, Xuna ^{[1
]}

Pang, Shuchun ^{[2
]}

Zhang, Meijing ^{[3
]}

Li, Shengyu ^{[1
]}

机构：

[1] Hebei Univ Technol, Engn Res Ctr Seawater Utilizat Technol, Tianjin 300130, Peoples R China

[2] Hebei Univ Technol, Sch Comp Sci & Engn, Tianjin 300130, Peoples R China

[3] Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300072, Peoples R China

来源：

ADVANCES IN CHEMICAL ENGINEERING, PTS 1-3 | 2012年 / 396-398卷

关键词：

wavelet neural network; prediction; cortisol; solubility; APPROXIMATION;

D O I：

10.4028/www.scientific.net/AMR.396-398.711

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Wavelet neural network(WNN) was applied to predicate the cortisol solubility. The model consists of a multilayer feedforward hierarchical structure, and the flow of information is directed from the input to the output layer by using wavelet transforms to achieve faster convergence. By adaptively adjusting the number of training data involved during training, an adaptive robust learning algorithm is derived for improvement of the efficiency of the network. The neural network was trained and simulated cortisol solubility with different input and output parameters. Simulation results confirmed that this approach gave more accurate predictions solubility.

引用

页码：711 / +

页数：2

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