A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes

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.

The role of fluid-wall interactions on confined liquid diffusion using Mori theory

“…The numerical calculations for the sum rules V 2 and V 42 given by Eqs. (23), (25), (28), (30), and (36)- (39) are carried out by using the Gauss-quadrature method. The radial distribution function for fluid-fluid interaction as well as for fluid-wall interaction was taken to be same as that of bulk fluid.…”

“…[14][15][16][17] Recent studies suggest that the transport properties of fluid in confined nanogeometries show anisotropic behavior. [18][19][20][21][22] The self-diffusivity of fluids confined in various nanogeometries, such as slit shaped nano-pores, [23][24][25][26] cylindrical nanotube, 27,28 and carbon nanotube [29][30][31][32][33] have been widely investigated using molecular dynamics simulation. It is observed that the mobility of particles in a nano pore is maintained even in pore widths as small as two times of size of particles.…”

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

“…Whether to consider the time factor or not, the use of static neural network can be found widely in various applications (e.g. [16][17][18][19][20]). Relatively, the time factor is explicit represented in the dynamic neural network model, by using feedback loop to cause time delays.…”

Thermal Error Modelling of the Spindle Using Neurofuzzy Systems

“…For getting better predictive effect, we use ANFIS to perform the adjustment of the combined model. ANFIS is a fuzzy inference system (FIS) implemented as a neural network, firstly proposed by Jang [34] in 1993, and then has been widely used [35][36][37]. It is a five layered feed-forward neural network structure and uses fuzzy reasoning and neural network learning algorithms to map inputs into an output.…”