The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method exploits the fact that any marginal distribution of a defined Gaussian joint distribution can be determined from the mean vector and covariance matrix of the joint distribution. The method is discussed in the context of complete and incomplete tra...

We present different training algorithms for radial basis function (RBF) networks and the behaviour of RBF classifiers in three different pattern recognition applications is presented: the classification of 3-D visual objects, highresolution electrocardiograms and handwritten digits.

Two new contributions are presented here. This paper proposes using a Model Predictive Control (MPC) incorporating a Radial Basis Function (RBF) Network Observer for the fuel injection problem. Firstly a RBF Network is used as an observer for the volumetric efficiency of the air system. This allows for gradual adaptation of the observer, ensuring the control scheme is capable of maintaining goo...

Automatic recognition of signature is a challenging problem which has received much attention during recent years due to its many applications in different fields. Signature has been used for long time for verification and authentication purpose. Earlier methods were manual but nowadays they are getting digitized. This paper provides an efficient method to signature recognition using Radial Bas...

This paper discusses a discretisation scheme which is based on point collocation and integrated radial basis function networks (IRBFNs) for the solution of elliptic differential equations (DEs). The use of IRBFNs to represent the field variable in a given DE gives several advantages over the case of using conventional RBFNs and polynomials. Some numerical examples are included for demonstration...

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the...

The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF network...

Abstract: The performances of Normalised RBF (NRBF) nets and standard RBF nets are compared in simple classification and mapping problems. In Normalized RBF networks, the traditional roles of weights and activities in the hidden layer are switched. Hidden nodes perform a function similar to a Voronoi tessellation of the input space, and the output weights become the network's output over the pa...

In this study, we are mainly interested in error estimates of interpolation, using smooth radial basis functions such as multiquadrics. The current theories of radial basis function interpolation provide optimal error bounds when the basis function φ is smooth and the approximand f is in a certain reproducing kernel Hilbert space Fφ. However, since the space Fφ is very small when the function φ...

This paper focuses on a training-based method to reconstruct a scene’s spectral reflectance from a single RGB image captured by a camera with known spectral response. In particular, we explore a new strategy to use training images to model the mapping between cameraspecific RGB values and scene reflectance spectra. Our method is based on a radial basis function network that leverages RGB white-...