東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Feed Forward Neural Network Approach...

Khan, Kamran Ahmed.

FindBook

Google Book

Amazon

博客來

Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits./
作者:	Khan, Kamran Ahmed.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 82-06.
Contained By:	Masters Abstracts International82-06.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28260657
ISBN:	9798557010221

Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits.
Khan, Kamran Ahmed.

Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 57 p.

Source: Masters Abstracts International, Volume: 82-06.

Thesis (M.S.)--Texas A&M University - Kingsville, 2020.

This item must not be sold to any third party vendors.

This study explains a technique for modeling multiple faults in digital circuits developed on the basis of Neural Network. This study is performed by collecting information from models expressed in Verilog hardware description language. The method uses various quantities of chosen circuit test data, which is generated by introducing single stuck-at faults into the sequential circuit; the observed test vectors which is generated by using Design for Testability (DFT) are then applied to train models of neural network expressed in MATLAB. The neural network models trained are capable of replicating circuit behavior in the presence of faults. The research is based on a benchmark ISCAS-S27 sequential circuit. This approach generates more accurate models than previously published methods. In the presence of faults, the Artificial Neural Network (ANN) is able to replicate the circuit output.

ISBN: 9798557010221Subjects--Topical Terms:

649834
Electrical engineering.

Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits.
LDR:01855nmm a2200289 4500 001 2276794
005 20210510091912.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798557010221
035 $a (MiAaPQ)AAI28260657
035 $a AAI28260657
040 $a MiAaPQ $c MiAaPQ
100 1 $a Khan, Kamran Ahmed. $3 3555092
245 1 0 $a Feed Forward Neural Network Approach for Diagnosing the Faulty Functioning in Digital Circuits.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 82-06.
500 $a Advisor: Nekovei, Reza.
502 $a Thesis (M.S.)--Texas A&M University - Kingsville, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This study explains a technique for modeling multiple faults in digital circuits developed on the basis of Neural Network. This study is performed by collecting information from models expressed in Verilog hardware description language. The method uses various quantities of chosen circuit test data, which is generated by introducing single stuck-at faults into the sequential circuit; the observed test vectors which is generated by using Design for Testability (DFT) are then applied to train models of neural network expressed in MATLAB. The neural network models trained are capable of replicating circuit behavior in the presence of faults. The research is based on a benchmark ISCAS-S27 sequential circuit. This approach generates more accurate models than previously published methods. In the presence of faults, the Artificial Neural Network (ANN) is able to replicate the circuit output.
590 $a School code: 1187.
650 4 $a Electrical engineering. $3 649834
650 4 $a Circuits. $3 3555093
650 4 $a Fault diagnosis. $3 3555094
650 4 $a Neural networks. $3 677449
690 $a 0544
710 2 $a Texas A&M University - Kingsville. $b Electrical Engineering and Computer Science. $3 2102338
773 0 $t Masters Abstracts International $g 82-06.
790 $a 1187
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28260657