DocumentCode
1638157
Title
An application of neural net in decoding error-correcting codes
Author
Zeng, Gengsheng ; Hush, Don ; Ahmed, Nasir
Author_Institution
Dept. of Electr. Eng. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
fYear
1989
Firstpage
782
Abstract
A neural net architecture is implemented as a maximum-likelihood decoder. Any block binary code can be easily decoded. In a von Neumann computer, the computation of all the Hamming distances requires exponential time; however, polynomial-time is needed for the neural net, taking advantage of parallel computation. In fact, picking the maximum is a polynomial-time problem. Since the decoding problem is NP-complete, all other NP-complete problems may be solvable by neural nets
Keywords
computational complexity; decoding; error correction codes; neural nets; parallel architectures; Hamming distances computation; NP-complete; block binary code; error correcting code decoding; exponential time; maximum-likelihood decoder; neural net architecture; parallel computation; polynomial-time; von Neumann computer; Application software; Concurrent computing; Error correction codes; Hamming distance; Image coding; Linear code; Maximum likelihood decoding; Neural networks; Polynomials; Traveling salesman problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location
Portland, OR
Type
conf
DOI
10.1109/ISCAS.1989.100467
Filename
100467
Link To Document