Title :
Neural network error corrector for binary messages on hydro-acoustic channels
Author :
Machado, Remo Z. ; Tenorio, Manoel F. ; Silva, Jose R M
Author_Institution :
Petrobras R&D Center, Rio de Janeiro, Brazil
fDate :
10/1/1992 12:00:00 AM
Abstract :
An application of neural networks for the identification and correction of transmission errors in binary messages is described. The network is used as a classifier of detected hydroacoustic signals. It converts the signals into one of a possible alphabet of symbols. The algorithm used is a Hamming-type neural network classifier associated with the transmission of a Hamming code. This system can detect and correct all transmission errors if the number of errors is less than or equal to half the Hamming distance between transmitted symbols minus one. Symbols to be transmitted are chosen and associated to messages, assuring that bit-to-bit nonsimilarities result on the prescribed Hamming distance. The auto-associative error correcting scheme can be used to generate a teaching signal to a supervised learning equalizer tracking the channel nonstationary characteristics. The proposed system is intended for use in hydroacoustic communication applications and is undergoing sea tests
Keywords :
Hamming codes; acoustic signal processing; binary sequences; error correction codes; feedforward neural nets; learning systems; telecommunications computing; tracking; underwater sound; Hamming code; Hamming distance; auto-associative error correcting; binary messages; channel nonstationary characteristics; classifier; correction; hydro-acoustic channels; hydroacoustic communication; hydroacoustic signals; identification; neural networks; ocean sound propagation; supervised learning equalizer; teaching signal; tracking; transmission errors; Character generation; Education; Equalizers; Error correction; Hamming distance; Neural networks; Signal detection; Signal generators; Supervised learning; Underwater communication;
Journal_Title :
Oceanic Engineering, IEEE Journal of
