Radial basis function(RBF) methods have been actively developed in the last decades. The advantages of RBF methods are that these methods are mesh-free and yield high order accuracy if the function is smooth enough. The RBF approximation for discontinuous problems, however, deteriorates its high order accuracy due to the Gibbs phenomenon. With the Gibbs phenomenon in the RBF approximation, the ...

This contribution presents a new analysis of properties of the interpolation using Radial Bases Functions (RBF) related to large data sets interpolation. The RBF application is convenient method for scattered d-dimensional interpolation. The RBF methods lead to a solution of linear system of equations and computational complexity of solution is nearly independent of a dimensionality. However, t...

Interpolation or approximation of scattered data is very often task in engineering problems. The Radial Basis Functions (RBF) interpolation is convenient for scattered (un-ordered) data sets in k-dimensional space, in general. This approach is convenient especially for a higher dimension k > 2 as the conversion to an ordered data set, e.g. using tessellation, is computationally very expensive. ...

We applied a renal blood flow (RBF) quantification technique to pediatric data, which depends minimally on bolus shape, uses a conventional radiopharmaceutical 99mTc-DTPA, and generates a value for RBF as a percentage of cardiac output (RBF/CO). Mean RBF was 16.9% (+/- 4.8) for normal transplants, 13.1% (+/- 2.9) for transplants undergoing mild-to-moderate chronic rejection, 7.9% (+/- 1.3) for ...

An effective technique for identifying nonlinear discrete-time systems using raisedcosine radial basis function (RBF) networks is presented. Raised-cosine RBF networks are bounded-input bounded-output stable systems, and the network output is a continuously differentiable function of the past input and the past output. The evaluation speed of an n-dimensional raised-cosine RBF network is high b...

air pollution is a challenging issue in some of the large cities in developing countries. air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. several methods exist to analyze air quality. among them, we applied the dynamic neural network (tdnn) and radial basis function (rbf) methods to predict the concentrations of ground-level...

The receptor-binding factor (RBF) for the avian oviduct progesterone (Pg) receptor (PR) has previously been shown to be a unique 10-kDa nuclear matrix protein that generates high affinity PR-binding sites on avian DNA. This paper describes the use of Southwestern blot and DNA gel shift analyses with RBF protein to identify a minimal 54-base pair RBF-binding element in the matrix-associated regi...

We introduce a master–worker framework for parallel global optimization of computationally expensive functions using response surface models. In particular, we parallelize two radial basis function (RBF) methods for global optimization, namely, the RBF method by Gutmann [Gutmann, H.M., 2001a. A radial basis function method for global optimization. Journal of Global Optimization 19(3), 201–227] ...

The current fault diagnosis methods based on conventional BP neural network and RBF neural network exist long training time, slow convergence speed and low judgment accuracy rate and so on. In order to improve the ability of fault diagnosis, this paper puts forward a kind of fault diagnosis method based on RBF Neural Network improved by PSO algorithm. By using particle swarm algorithm’s heurist...

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find ...