DocumentCode
3044302
Title
Design of neural networks to tolerate the mixture of two types of faults
Author
Tohma, Yoshihiro ; Koyanagi, Yoichi
Author_Institution
Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan
fYear
1993
fDate
22-24 June 1993
Firstpage
268
Lastpage
277
Abstract
The authors present a design method of neural networks for optimization problems, which can tolerate the simultaneous existence of both stuck-at-zero and stuck-at-one faults. By using this new design method together with one presented earlier, neural networks can tolerate very well the mixture of the both types of faults as well as unidirectional faults.
Keywords
neural nets; neural networks; optimization problems; stuck-at-one faults; stuck-at-zero; unidirectional faults; Application software; Computer applications; Computer networks; Computer science; Design methodology; Design optimization; Fault tolerance; Neural networks; Recurrent neural networks; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Fault-Tolerant Computing, 1993. FTCS-23. Digest of Papers., The Twenty-Third International Symposium on
Conference_Location
Toulouse, France
ISSN
0731-3071
Print_ISBN
0-8186-3680-7
Type
conf
DOI
10.1109/FTCS.1993.627330
Filename
627330
Link To Document