In this paper, Radial Basis Function (RBF) method for interpolating two dimensional functions with localized features defined on irregular domain is presented. RBF points located inside the domain and on its boundary are chosen such that they are the image of conformally mapped points on concentric circles on a unit disk. On the disk, a fast RBF solver to compute RBF coefficients developed by K...

Two difficulties are involved with traditional RBF networks: the initial configuration of an RBF network needs to be determined by a trial-and-error method, and the performance suffers degradation when the desired locations of the center of the RBF are not suitable. A novel RBF network is proposed to overcome these difficulties. A new radial basis function is used for hidden nodes, and the numb...

This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on mult iobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorit...

A non-linear version of the multivariate statistical technique of canonical correlation analysis (CCA) is proposed through the integration of a radial basis function (RBF) network. The advantage of the RBF network is that the solution of linear CCA can be used to train the network and hence the training effort is minimal. Also the canonical variables can be extracted simultaneously. It is shown...

The boundary knot method is a recent truly meshfree boundary-type radial basis function (RBF) collocation scheme, where the nonsingular general solution is used instead of the singular fundamental solution to evaluate the homogeneous solution, while the dual reciprocity method is employed to the approximation of particular solution. Despite the fact that there are not nonsingular RBF general so...

AbstractThis paper describes an experiment on face recognition using a simple feature vector and Support Vector Machine (SVM) classifier. Polynomial and Radial Basis Function (RBF) kernels of SVM are used for classification. The dataset in this experiment consists of a set of images of eight different faces (eight classes) containing ten different images for a single class. The experiment is pe...

The Bayesian equalizer is implementable by a proper employment of a radial basis function (RBF) neural network, with the inverse filtering problem posed as a classification problem. The proposed approach allows that the transmission of information and the RBF training be accomplished in a simultaneous and uninterrupted way. Moreover, the channel estimation procedure remains an unimodal optimiza...

In this paper, an extension of our previous research focused on haptic rendering based on interpolation from precomputed data is presented. The technique employs the radial-basis function (RBF) interpolation to achieve the accuracy of the force response approximation, however, it assumes that the data used by the interpolation method are generated on-the-fly during the haptic interaction. The i...

This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance and simplification, which are parts of de-noising or compression. We applied an effectiv...