An algorithm for use with several meshless schemes is presented based on a local extremum diminishing property. The scheme is applied to the Euler equations in two dimensions. The algorithm is suitable for use with many meshless schemes, three of which are detailed here. First, a method based on Taylor series expansion and least squares is highlighted. Next, a similar least squares method is us...

in this research, the spatial distribution of electrical conductivity and ph of springs, ghanats and the base flow concentration places on the streams of central and south-west and central parts of the hamedan-bahar plain were evaluated. using different geostatistical methods such as kriging, minimum curvature, inverse distance, natural neighbor, local polynomial and radial basis functions, 108...

Radial basis function methods for interpolation can be interpreted as roughness-minimizing splines. Although this relationship has already been established for radial basis functions of the form g(r) = r and g(r) = r log(r), it is extended here to include a much larger class of functions. This class includes the multiquadric g(r) = (r 2 + c 2) 1=2 and inverse multiquadric g(r) = (r 2 + c 2) ?1=...

A subdomain variational inequality and its meshless linear complementary formulation are developed in the present paper for solving two-dimensional contact problems. The subdomain variational inequality will be defined in detail. The meshless method is based on a local weighted residual method with the Heaviside step function as the weighting function over a local subdomain and radial basis fun...

To restore a degraded image, which has been corrupted by some kinds of noise and motion blur, a model for restoration is first presented and then an algorithm based on this model and the radial basis function network (RBFN) is proposed in this paper. In the first step of this algorithm, noise is removed by using the RBFN interpolation with variable regularization parameters. In the second step,...

The reconstruction of spectral function from correlation in Euclidean space is a challenging task. In this paper, we employ the Machine Learning techniques terms radial basis functions networks to reconstruct finite number data. To test our method, first generate one type data using mock by mixing several Breit-Wigner propagators. We found that compared with other traditional methods, TSVD, Tik...

Support Vector Machine (SVM) is one of the most robust and accurate method amongst all the supervised machine learning techniques. Still, the performance of SVM is greatly influenced by the selection of kernel function. This research analyses the characteristics of the two well known existing kernel functions, local Gaussian Radial Basis Function and global Polynomial kernel function. Based on ...

in this paper we use radial basis functions to solve multivariable integral equations. we use collocation method for implementation. numerical experiments show the accuracy of the method.