In this paper, the combination of RBF (Radial Basis Function) neural network and sliding mode control, which is used for vibration control, is examined. The approach is based on a sliding mode control methodology which drives the system towards a sliding surface by tuning the parameters of the controller using Gaussian radial basis function neural network. The input and output of RBF neural net...

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...

Partial differential equations (PDEs) on surfaces arise in a variety of application areas including biological systems, medical imaging, fluid dynamics, mathematical physics, image processing and computer graphics. In this paper, we propose a radial basis function (RBF) discretization of the closest point method. The corresponding localized meshless method may be used to approximate diffusion o...

Mesh-free methods based on radial basis function (RBF) approximation are widely used for solving PDE problems. They are flexible with respect to the problem geometry and highly accurate. A disadvantage of these methods is that the linear system to be solved becomes dense for globally supported RBFs. A remedy is to introduce localisation techniques such as partition of unity (PU). RBF-PU methods...

In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using th...

This work extends the application of Radial Basis Function (RBF) neural network for the unsupervised classification of images. The radial basis function (RBF) network enables non-linear transformation followed by linear transformation to achieve a higher dimension in the hidden space. If classification is done in a high dimensional space, it is more likely to be linearly separable as compared t...

Low resolution iris image can degrade the performance of iris recognition. A new iris image zooming method by using edge-directed image upsampling method based on radial basis function (RBF) interpolation [9] is proposed to restore a single iris image into a highresolution image. By increasing the resolution of iris image, we found that the performance of iris recognition was increased.

In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by differen...