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Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering
นักวิจัย : Li, Michael M. , Fan, Xiaolong. , Tickle, Kevin.
คำค้น : 671401 Scientific instrumentation , 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic , Spectrum analysis. , 861503 Scientific Instruments. , 8615 Instrumentation. , 86 Manufacturing. , 080108 Neural, Evolutionary and Fuzzy Computation. , 0801 Artificial Intelligence and Image Processing. , 08 Information and Computing Sciences. , Neural networks -- Resilient backpropagation -- Principal component analysis
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2549
อ้างอิง : http://hdl.cqu.edu.au/10018/7730 , cqu:415
ที่มา : Li, M, Fan, X & Tickle, K 2006, 'Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering ', Proceedings of the 24th International Multi-Conference on Artificial Intelligence and Applications (IASTED), February 13-16, 2006, Innsbruck, Austria, pp. 228-234.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of the 24th International Conference on Artificial Intelligence and Applications (IASTED), 13-16 February 2006, Innsbruck, Austria. Calgary, Canada. : ACTA Press, 2006. p. 228-234 7 pages Refereed 0889865582 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The problem of ion backscattering spectral data analysis, which is to determine the physical structure of a sample from the measured spectra, was studied with neural network techniques. A new method based on principal component analysis was proposed to compress the number of nodes in the input layer so that the dimensionality of spectral data was significantly reduced. This provides a fast convergence within reasonable size of training set. The constructed neural network was applied to some computation examples, in which backscattering spectra from SiGe thin films on a silicon substrate were discussed in details. The network was trained by the resilient backpropagation algorithm with hundreds of simulated spectra of samples for which the structures were known. The trained network also was tested to analyse spectra with unknown structure of samples. The neural network prediction results were accurate within error of 5.5% and this may suggest that the approach of combining neural network and principal component analysis could be a potential tool of analysis and prediction for non-experts. The proposed approach can handle properly redundancies, which were caused by the constraint of output variables.

บรรณานุกรม :
Li, Michael M. , Fan, Xiaolong. , Tickle, Kevin. . (2549). Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Li, Michael M. , Fan, Xiaolong. , Tickle, Kevin. . 2549. "Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Li, Michael M. , Fan, Xiaolong. , Tickle, Kevin. . "Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2549. Print.
Li, Michael M. , Fan, Xiaolong. , Tickle, Kevin. . Principal component analysis and neural networks for analysis of complex spectral data from ion backscattering. กรุงเทพมหานคร : Central Queensland University, Australia; 2549.