In this paper, a hybrid network based on the combination of Radial Basis Function Networks (RBFNs) and Gaussian Mixture Models (GMMs) is proposed and used for speaker recognition. The hybrid network is a hierarchical one, where a GMM is built for each speaker and an RBFN is built for each group of speakers. The GMMs and RBFNs are trained independently. The RBFNs are used as a rst stage coarse c...

This paper presents a new data-driven approach for modeling haptic responses of textured surfaces with homogeneous anisotropic grain. The approach assumes unconstrained tool-surface interaction with a rigid tool for collecting data during modeling. The directionality of the texture is incorporated in modeling by including 2 dimentional velocity vector of user’s movement as an input for the data...

An orthogonal forward selection (OFS) algorithm based on the leaveone-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to sp...

Normalisation of the basis function activations in a radial basis function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisa-tion of the basis functions can lead to other eeects which are sometimes less desireable for modelling ...

A Radial Basis Function Network (RBFN) classifier for hard fault location in CMOS analogue circuit is presented. The network is trained by means of a fault dictionary containing the faulty circuit response, which is obtained by simulating the supply current dynamic response.

We present probabilistic models which are suitable for class conditional density estimation and can be regarded as shared kernel models where sharing means that each kernel may contribute to the estimation of the conditional densities of an classes. We first propose a model that constitutes an adaptation of the classical radial basis function (RBF) network (with full sharing of kernels among cl...

This paper presented the Minimal Radial Basis Function Network (MRBFN) approach busbar protection. The Optical Current Transducer (OCT) is used to solve the magnetic saturation so as to improve the reliability of the system. Performance of this model is compared with Feed Forward Back Propagation Neural Network (FFBP). The proposed model is more accurate in prediction with few numbers of hidden...

In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases. A sequential input selection algorithm for Radial basis function (SISAL-RBF) networks is presented to analyze importances of the inputs. The ranking of inputs is based on values, which are evaluated from the partial ...

The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: 1) the necessary and sufficient condition for a function of one variable to be qualified as an activation function in RBF network is that the function is not an even polynomial, and 2) the capability of approximation to nonlinear functionals and operators b...

While learning an unknown input-output task, humans rst strive to understand the qualitative structure of the function. Accuracy of performance is then improved with practice. In contrast, existing neural network function approximators do not have an explicit means for abstracting the qualitative structure of a target function. To ll this gap, we introduce the concept of function emulation, acc...