Many numerical and analytical schemes exist for solving heat transfer problems. The meshless method is a particularly attractive method that is receiving attention in the engineering and scientific modeling communities. The meshless method is simple, accurate, and requires no polygonalisation. In this study, we focus on the application of meshless methods using radial basis functions (RBFs) – w...

Abstract The goal of this work is to develop a numerical method combining Radial Basic Functions (RBF) kernel and high order algorithm based on Taylor series homotopy continuation method. local RBF approximation applied in strong form allows us overcome the difficulties integration treat problems large deformations. Furthermore, enables transform nonlinear problem set linear problems. Determini...

This work presents a novel approach for classification of both balanced and unbalanced dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to a radial basis function may greatly enhance the network efficiency (in terms of accuracy) at the same time compact it size. In this paper,...

Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can be spectrally accurate. A perceived practical obstacle is that the interpolation matrix becomes increasingly illconditioned as the RBF shape parameter becomes small, corresponding to flat RBFs. Two stable approaches that overco...

Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can be spectrally accurate. A perceived practical obstacle is that the interpolation matrix becomes increasingly illconditioned as the RBF shape parameter becomes small, corresponding to flat RBFs. Two stable approaches that overco...

In this paper we present an integer programming formulation of the minimum sphere covering problem that seeks to construct a minimum number of spheres to represent the training data. Using soft threshold functions, we further derive a linear programming problem whose solution gives rise to radial basis function classifiers and sigmoid function classifiers. In contrast to traditional RBF and sig...

The parabolic partial differential equation arises in many application of technologies. In this paper, we propose an approximate method for solution of the heat and advection-diffusion equations using Laguerre-Gaussians radial basis functions (LG-RBFs). The results of numerical experiments are compared with the other radial basis functions and the results of other schemes to confirm the validit...

Based on radial basis function neural network (RBF NN),the paper proposed a new algorithm for strip shape recognition. Compared with back propagation (BP) algorithm and improved least squares method (LSM), RBF NN shows excellent overall performance, such as learning speed, recognition precision and anti-interference capability. Copyright©2005IFAC Keyword: Strip Shape, Pattern Recognition, RBF,B...

in this paper, we propose a new numerical method for solution of urysohn two dimensional mixed volterra-fredholm integral equations of the second kind on a non-rectangular domain. the method approximates the solution by the discrete collocation method based on inverse multiquadric radialbasis functions (rbfs) constructed on a set of disordered data. the method is a meshless method, because it i...

A radial basis function ( RBF ) neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In this paper we have proposed an algorithm for RBF neural network and the results may be reduced for artificial neural networks as particular cases.