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Gaussian Mixture Modeling Versus Auto Associative Neural Network For Analog Circuits Fault Diagnosis / Mohamed Ahmed Aboelsoud Aboelmagd ; Supervised Mohamed Abd El aziz Elgamal

By: Contributor(s): Language: Eng Publication details: Cairo : Mohamed Ahmed AboelsSoud AboelmMagd , 2006Description: 139P : charts ; 30cmOther title:
  • مقارنة بين طريقتى نموذج خليط جاوس والشبكات العصبية ذات المشاركة الاوتوماتية فى تشخيص الاعطال فى الدوائر التناظرية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty Of Engineering - Department Of Mathematics and Physics Summary: A new fault Diagnosis procedure for analog circuits is presented The remarkable abilities of the Gaussian Mixture Model (GMM) and the Auto Associative Neural Network (AANN) to model arbitrary densities are exploited in isolating analog circuits' faults The innovation aspect of the proposed approaches is the use of new training techniques for the GMM and the introduction of the AANN model in the analog fault diagnosis problem
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Item type Current library Home library Call number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.10.M.Sc.2006.Mo.G. (Browse shelf(Opens below)) Not for loan 01010110046401000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.10.M.Sc.2006.Mo.G. (Browse shelf(Opens below)) Not for loan 01020110046401000

Thesis (M.Sc.) - Cairo University - Faculty Of Engineering - Department Of Mathematics and Physics

A new fault Diagnosis procedure for analog circuits is presented The remarkable abilities of the Gaussian Mixture Model (GMM) and the Auto Associative Neural Network (AANN) to model arbitrary densities are exploited in isolating analog circuits' faults The innovation aspect of the proposed approaches is the use of new training techniques for the GMM and the introduction of the AANN model in the analog fault diagnosis problem

Issued also as CD

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