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RBF neural networks for solving the inverse problem of backscattering spectra

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : RBF neural networks for solving the inverse problem of backscattering spectra
นักวิจัย : Li, Michael M. , Verma, Brijesh. , Fan, Xiaolong. , Tickle, Kevin.
คำค้น : TBA , Backscattering. , Spectrum analysis. , 740301 Higher education , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , Radial basis function -- Inverse problems -- Neural networks -- RBS -- Spectral data analysis
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2551
อ้างอิง : http://hdl.cqu.edu.au/10018/17753 , cqu:3187
ที่มา : Li, M, Verma, B, Fan, X & Tickle, K 2008 ' RBF neural networks for solving the inverse problem of backscattering spectra', Neural Computing & Applications, vol.17, no. 4, pp. 391-397. http://dx.doi.org/10.1007/s00521-007-0138-2
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Neural computing & applications London : Springer, 2008. Vol. 17, No. 4 (August 2008), p. 391-397 8 pages Refereed 1433-3058 0941-0643 (online) , aCQUIRe [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

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.

บรรณานุกรม :
Li, Michael M. , Verma, Brijesh. , Fan, Xiaolong. , Tickle, Kevin. . (2551). RBF neural networks for solving the inverse problem of backscattering spectra.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Li, Michael M. , Verma, Brijesh. , Fan, Xiaolong. , Tickle, Kevin. . 2551. "RBF neural networks for solving the inverse problem of backscattering spectra".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Li, Michael M. , Verma, Brijesh. , Fan, Xiaolong. , Tickle, Kevin. . "RBF neural networks for solving the inverse problem of backscattering spectra."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2551. Print.
Li, Michael M. , Verma, Brijesh. , Fan, Xiaolong. , Tickle, Kevin. . RBF neural networks for solving the inverse problem of backscattering spectra. กรุงเทพมหานคร : Central Queensland University, Australia; 2551.