首页 | 本学科首页   官方微博 | 高级检索  
     


RBF neural networks for solving the inverse problem of backscattering spectra
Authors:Michael M Li  Brijesh Verma  Xiaolong Fan  Kevin Tickle
Affiliation:(1) School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD, 4702, Australia
Abstract: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 the 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.
Keywords:Radial basis function  Inverse problems  Neural networks  RBS  Spectral data analysis
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号