SUMMARY Representing the concept of numerical data by linguistic rules is often desir­ able. In this paper, we present a novel rule-extraction algorithm from the radial basis function (RBF) neural network classifier for representing the hidden concept of numerical data. Gaussian function is used as the basis function of the RBF network. When training the RBF neural network, we allow for large o...

We present a novel numerical method for solving ordinary differential equations using radial basis function (RBF) network with extreme learning machine algorithm. A single-layer RBF link neural model has been developed the proposed method. The weight from hidden layer to output can be calculated efficiently by experimental comparison of various methods proves that shows better performance than ...

This paper will demonstrate how European and American option prices can be computed under the jump-diffusion model using the radial basis function (RBF) interpolation scheme. The RBF interpolation scheme is demonstrated by solving an option pricing formula, a one-dimensional partial integro-differential equation (PIDE). We select the cubic spline radial basis function and adopt a simple numeric...

| This paper presents a fast orthogo-nalization process to train a Radial Basis Function (RBF) neural network. The traditional methods for connguring the RBF weights is to use some matrix inversion or iterative process. These traditional approaches are either time consuming or computationally expensive, and often do not converge to a solution. The goal of this paper is rst to use a fast orthogo...

Abstract This paper proposes a local meshless radial basis function (RBF) method to obtain the solution of two-dimensional time-fractional Sobolev equation. The model is formulated with Caputo fractional derivative. uses RBF approximate spatial operator, and finite-difference algorithm as time-stepping approach for in time. stability technique examined by using matrix method. Finally, two numer...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

The use of the K-means algorithm and the K-nearest neighbor heuristic in estimating the radial basis function (RBF) parameters may produce sub-optimal performance when the input vectors contain correlated components. This paper proposes to overcome this problem by incorporating full covariance matrices into the RBF structure and to use the expectation-maximi-zation (EM) algorithm to estimate th...

In the present study, the reliability assessment of performance-based optimally seismic designed reinforced concrete (RC) and steel moment frames is investigated. In order to achieve this task, an efficient methodology is proposed by integrating Monte Carlo simulation (MCS) and neural networks (NN). Two NN models including radial basis function (RBF) and back propagation (BP) models are examine...

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

In this paper, a new kernel function is introduced that improves the classification accuracy of support vector machines (SVMs) for both linear and non-linear data sets. The proposed kernel function, called Gauss radial basis polynomial function (RBPF) combine both Gauss radial basis function (RBF) and polynomial (POLY) kernels. It is shown that the proposed kernel converges faster than the RBF ...