Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/54709
Title: Application of the LM-trained Model for Predicting the Retardance of Citrate Coated Ferrofluid
Authors: Lin, Jing-Fung
Keywords: Artificial Neural Network;Multiple Regression;Nanoparticle;Retardance
Issue Date: May-2020
Publisher: NISCAIR-CSIR, India
Abstract: 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.
Page(s): 369-371
URI: http://nopr.niscair.res.in/handle/123456789/54709
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(05) [May 2020]

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