A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through
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RESEARCH PAPER
A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes R. Kamali • A. R. Binesh
Received: 17 July 2012 / Accepted: 6 October 2012 / Published online: 18 October 2012 Ó Springer-Verlag Berlin Heidelberg 2012
Abstract Given the fact that artificial intelligence tools such as neural network and fuzzy logic are capable of learning and inferencing from the past to capture the patterns that exist in the data, this study presents an intelligent method for the forecasting of water diffusion through carbon nanotubes where predictions are generated from neurofuzzy structures using molecular dynamics data. Therefore, this research was mainly focused on combining molecular dynamics with artificial intelligence methods in order to reduce the computational time of biomolecular and nanofluidic simulations. Two different artificial intelligence methods are applied for the time-dependent water diffusion forecasting: artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). The effects of different sizes of training sample sets on forecasting performance of ANN and ANFIS are investigated as well. Four different evaluation methods are used to measure the performance and forecasting accuracy of these two methods. As a result, ANFIS presents the higher accuracy than neural network method based on the comparison of these different evaluation methods adopted in this research. The results reported in this research demonstrate that combining of molecular dynamics with artificial intelligence methods can be one of the most powerful and beneficial tools for prediction of important nanofluidic parameters. Keywords ANN
Water diffusion Carbon nanotubes ANFIS
R. Kamali (&) A. R. Binesh Department of Mechanical Engineering, Shiraz University, 71348-51154 Shiraz, Iran e-mail: [email protected] A. R. Binesh e-mail: [email protected]
1 Introduction Nanofluidic is a new subject in the field of nanoscience and nanotechnology, but the study of fluid in the nanoscale channels is related to many other research areas such as biology, chemistry, and mechanical engineering. There are many applications of water transport through carbon nanotubes at the interface of biotechnology and nanotechnology. For instance, fluid flow through narrow biological channels can be found in every living cell. Carbon nanotubes could be used as artificial membranes which would act as biological channels and aquaporins. These biological channels are very important in transporting water molecules across cells such as the red blood cells or the kidneys. Transport of water through carbon nanotubes can also be used for water purification or desalination and in nanofluidic devices such as lab-on-the-chip probes. Therefore, the study of nanoscale effects on water transport through nanochannels is an important aspect and has been widely investigated by researchers both experimentally (Holt et al. 2006; Hummer et al. 2001; Majumder et al. 2005)
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