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...

To assess the reliability of electron beam computed tomography (EBCT), measurements of single-kidney renal blood flow (RBF), glomerular filtration rate (GFR), and intratubular contrast medium concentration (ITC) of radiographic contrast media were quantified in anesthetized pigs before and after acetylcholine-induced vasodilation and diuresis. EBCT measurements were compared with those obtained...

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. T...

This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBFARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending on the working-points of a system. The ...

Traditional finite difference (FD) methods are designed to be exact for low degree polynomials. They can be highly effective on Cartesian-type grids, but may fail for unstructured node layouts. Radial basis function-generated finite difference (RBF-FD) methods overcome this problem and, as a result, provide a much improved geometric flexibility. The calculation of RBF-FD weights involves a shap...