Non-Math-Modelling Data Analytical Methods Base on Artificial Neural Networks (ANNs) Applied to Optimize Preparation of Norcantharidin-Loaded Chitosan Nanoparticles

Abstract:

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The artificial neural networks (ANNs) non-modeling methods were selected to optimize the preparation of loading norcantharidin chitosan nanoparticles (NPs) by ionic cross-linkage. A multiple regression model was constructed for fitting several preparation factors and each of the factor level values was arranged in the L9(34) design table and their linear weighted sum of the normalized value was taken as optimized object. A Back-Propagation (BP) network (3×7×2) in ANNs was created and trained for further checking the optimal results and the trained network was applied to simulate the experiment system and screen the optimal conditions. Finally, the best condition was obtained.

Info:

Periodical:

Advanced Materials Research (Volumes 443-444)

Edited by:

Li Jian

Pages:

319-324

DOI:

10.4028/www.scientific.net/AMR.443-444.319

Citation:

Y. Liu et al., "Non-Math-Modelling Data Analytical Methods Base on Artificial Neural Networks (ANNs) Applied to Optimize Preparation of Norcantharidin-Loaded Chitosan Nanoparticles", Advanced Materials Research, Vols. 443-444, pp. 319-324, 2012

Online since:

January 2012

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Price:

$35.00

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