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Application of the LM-trained Model for Predicting the Retardance of Citrate<br />Coated Ferrofluid

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Title Statement Application of the LM-trained Model for Predicting the Retardance of Citrate<br />Coated Ferrofluid
 
Added Entry - Uncontrolled Name Lin, Jing-Fung ; Department of Industrial Design, Far East University, Taiwan, R.O.C. Address: No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74 448, Taiwan R.O.C.
 
Uncontrolled Index Term Artificial Neural Network; Multiple Regression; Nanoparticle; Retardance
 
Summary, etc. This paper has focused on developing an optimized artificial neural network (ANN) modeling to highly predict the retardance in ferrofluid of citrate coated magnetite nanoparticles. Utilizing the previously measured retardances as a training dataset, ANN models in architectures of single/double-hidden layer were trained by the Levenberg-Marquardt (LM) algorithm. From the testing of neural network with double-hidden layer (4-4-30-1); the correlation coefficient and average absolute relative error of predicted/simulated (ANN/multiple regression) retardance values were determined as 0.878 and 1.19%, respectively. Hence, a highly predictive LM-trained ANN model is obtained.
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2020-07-29 12:49:53
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/38223
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 05
 
Language Note en