Directional Wind Spectrum Description using Bivariate L1 Norm RBFs

In this paper, the simplest directional wind spectrum description is given using surrogate bivariate polynomial radial basis functions (PRBF) with L1 norm smoothed by dense boundary points distribution, which enables an accurate description of the geometry and the calculation of the volume below the observed surface when belonging double integral is known. For that purpose, the direct solution of double integral below the descriptive surface is given for bivariate polynomial RBFs with integer exponents, which is examined for accuracy on two examples, for Franke’s 2D function and upper hemisphere. After proven accurate in those examples, the direct description of the directional wind spectrum and the calculation of the joint density function of the wind spectrum is done in the paper, thus proving PRBFs as an efficient method for wind spectrum description. In that way, it is possible to calculate the joint density function (JDF) of the actual measured directional wind spectrum analytically, instead of the theoretical calculations used so far

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford Backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford Backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem