In this paper, we introduce a new kernel function called polynomial radial basis function (PRBF) that could improve the classification accuracy of support vector machines (SVMs). The proposed kernel function combines both Gauss (RBF) and Polynomial (POLY) kernels and is stated in general form. It is shown that the proposed kernel converges faster than the Gauss and Polynomial kernels. The accur...

in this paper, we present a computational method for solving boundary integral equations with loga-rithmic singular kernels which occur as reformulations of a boundary value problem for the laplacian equation. themethod is based on the use of the galerkin method with cas wavelets constructed on the unit interval as basis.this approach utilizes the non-uniform gauss-legendre quadrature rule for ...

In this paper, a new partition of unity ± the synchronized reproducing kernel (SRK) interpolant ± is derived. It is a class of meshless shape functions that exhibit synchronized convergence phenomenon: the convergence rate of the interpolation error of the higher order derivatives of the shape function can be tuned to be that of the shape function itself. This newly designed synchronized reprod...

A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper. The algorithm applies linear regressive model to the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared-error regressor. The algorithm includes two steps: the first step is to select the feat...

Wavelets are explored as a data smoothing (or de-noising) option for solution monitoring data in nuclear safeguards. In wavelet-smoothed data, the Gibbs phenomenon can obscure important data features that may be of interest. This paper compares wavelet smoothing to piecewise linear smoothing and local kernel smoothing, and illustrates that the Haar wavelet basis is effective for reducing the Gi...

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are ‘flat’ leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vec...

A new method that researching fault diagnosis of high-voltage (HV) circuit breaker (CB) is proposed. The method combines Wavelet Packet (WP) with Radical Basis Function (RBF) Neural Network (NN). Firstly, by applying the theory of WP decomposition and reconstruction, the mechanical vibration signal of CB was decomposed into different frequency bands, and the coefficients are reconstructed in th...

Wavelet theory has a profound impact on signal processing as it offers a rigorous mathematical framework to the treatment of multiresolution problems. The combination of soft computing and wavelet theory has led to a number of new techniques. On the other hand, as a new generation of learning algorithms, support vector regression (SVR) was developed by Vapnik et al. recently, in which ε-insensi...

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; ...