Multilayer feedforward networks are universal approximators
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Expert Systems with Applications: An International Journal
Application of artificial intelligence CFD based on neural network in vapor-water two-phase flow
Engineering Applications of Artificial Intelligence
Advanced neural-network training algorithm with reduced complexity based on Jacobian deficiency
IEEE Transactions on Neural Networks
Neighborhood based Levenberg-Marquardt algorithm for neural network training
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the FNN. The equations were solved using finite differences method by writing a FORTRAN code. The Levenberg-Marquardt algorithm is used to train the neural network. The optimal FNN architecture was determined. The FNN predicted values are in accordance with the values obtained by the finite difference method (FDM). The performance of the neural network model was assessed through the correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). The respective values of r, MAE and MSE for the testing data are 0.9999, 0.0025 and 1.9998.10^-^5.