NDLI: Artificial Neural Network(ANN)-based nonlinear optimization of modeling on biomicrofluidic vesicles generation

Content Provider	IEEE Xplore Digital Library
Author	Peiyuan He Lexun Xue Yuanming Qi Li Zhang Yumin Lu
Copyright Year	2013
Description	Author affiliation: Zhengzhou Univ., Zhengzhou, China (Peiyuan He; Lexun Xue; Yuanming Qi; Li Zhang; Yumin Lu)
Abstract	Biomicrofluidics has been an effective tool to manipulate vesicles at micrometer-scale since the last decade, particularly for the monodisperse microemulsions production. In this work, the flow regime of vesicle generation was studied in the biomicrofluidic environment for the numerical description of microvesicle size variation. Biomi-crofluidic vesicles generation is a complicated process for the study on fluidic silhouette, evolution mechanism, as well as fluidic manipulation at micrometer-scale. Modeling of the descriptive observation permits to understand the inside mechanism of these phenomena. Both linear modeling and optimized nonlinear modeling were introduced. The rough linear model to express vesicle generation was found to be somewhat short of effectiveness. Artificial Neural Network(ANN) technology was applied to perform nonlinear optimization. The results from verification procedures confirmed the improved descriptive quality for this nonlinear model. Besides, to our knowledge, this work for the first time introduced ANN technology to ameliorate vesicle production for pharmaceutical application as well as life science.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	267
Ending Page	271
File Size	144502
Page Count	5
File Format	PDF
ISBN	9781467347143
ISSN	21579563
DOI	10.1109/ICNC.2013.6817983
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Biological system modeling Artificial neural networks Production Numerical models Mathematical model Biological neural networks
Content Type	Text
Resource Type	Article

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