An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) misclassification rate is proposed for the construction of radial basis function (RBF) classifiers with tunable units. Each stage of the construction process determines a RBF unit, namely its centre vector and diagonal covariance matrix as well as weight, by minimising the LOO statistics. This OFS-LOO algorithm is comp...

The performance of the proposed radial basis function (RBF) assisted turbo-coded adaptive modulation scheme is characterized in a wideband channel scenario. We commence by introducing the novel concept of the Jacobian RBF equalizer, which is a reduced-complexity version of the conventional RBF equalizer. Specifically, the Jacobian logarithmic RBF equalizer generates its output in the logarithmi...

Approximation methods are widely used in many fields and many techniques have been published already. This comparative study presents a comparison of LOWESS (Locally weighted scatterplot smoothing) and RBF (Radial Basis Functions) approximation methods on noisy data as they use different approaches. The RBF approach is generally convenient for high dimensional scattered data sets. The LOWESS me...

This paper describes a novel adaptive noise cancellation system with fast tunable radial basis function (RBF). The weight coefficients of the RBF network are adapted by the multi-innovation recursive least square (MRLS) algorithm. If the RBF network performs poorly despite of the weight adaptation, an insignificant node with little contribution to the overall performance is replaced with a new ...

A novel method for constructing RBF networks is presented. It is based on Boosting, an ensemble method that combines several classifiers obtained using any other classification method. If the classifiers that are going to be combined by boosting are radialbasis functions, then the boosting method produces a RBF network as result. The method for constructing a RBF is based on obtaining a decisio...

Radial basis function generated finite differences (RBF-FD) is a mesh-free method for numerically solving partial differential equations (PDEs) that emerged in the last decade and has shown rapid growth in the last few years. From a practical standpoint, RBF-FD sprouted out of global RBF methods, which have shown exceptional numerical qualities in terms of accuracy and time stability for numeri...

In this paper we will discuss a 1D learner based on RBF (Radial Basis Functions) networks. It differs from the classical RBF networks by the selection of cluster centers dependent of class classification in the training set. After the method is described, it is compared to other methods (KNN, RBF) and improvement suggestions are made.

To assess the roles of feeding behavior (eating and rumination) and systemic arterial pressure (SAP) on determination of the circadian rhythm of renal blood flow (RBF), 20 sheep fitted with ultrasonic flow-metering probes around both renal arteries and a submandibular balloon to monitor jaw movements (6 of them with a telemetry measurement system into the carotid artery for SAP recording), were...

ii Preface Radial Basis Function (RBF) methods have become the primary tool for interpolating multidimensional scattered data. RBF methods also have become important tools for solving Partial Differential Equations (PDEs) in complexly shaped domains. Classical methods for the numerical solution of PDEs (finite difference, finite element, finite volume, and pseudospectral methods) are based on p...

The traditional RBF neural network has the problem of slow training speed and low efficiency, this paper puts forward the algorithm of improvement of RBF neural network by AdaBoost algorithm combined with PSO, to expand the application range of the RBF neural network. Firstly, it preprocesses the sample data in training set, and initialize the weights of test data; Secondly, it optimizes and ch...