An application of neural net in decoding error-correcting codes
Ganmin Zeng, Donald R. Hush, N. Ahmed · 2003
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.>