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

Yang Liu; Wei Zhang; Xue Nong Zhang

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

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 443-444Non-Math-Modelling Data Analytical Methods Base on...

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

Abstract:

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.

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Periodical:

Advanced Materials Research (Volumes 443-444)

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319-324

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https://doi.org/10.4028/www.scientific.net/AMR.443-444.319

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Online since:

January 2012

Authors:

Yang Liu, Wei Zhang, Xue Nong Zhang

Keywords:

Artificial Neural Network (ANN), Chitosan (CS), Nanoparticle, Norcantharidin, Orthogonal Design

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