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
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;
Conference_Titel :
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location :
Portland, OR
DOI :
10.1109/ISCAS.1989.100467
