High-order neural models for error-correcting code

Clark D. Jeffries; Peter Protzel

doi:10.1117/12.21202

1 August 1990 High-order neural models for error-correcting code

Clark D. Jeffries, Peter Protzel

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21202
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

The decoding and error-correction of data transmitted over a noisy channel is in principle equivalent to the operation of a neural network performing as a content-addressable memory. For a successful application however the neural network has to be capable of storing arbitrary words and it has to be guaranteed that the stored words represent the only stable attractors of the memory. In this paper we present a novel high order neural network architecture that has these characteristics. The analog nature of the network can be used to perform softdecision decoding with any block code. The performance in terms of post-decoding bit error rate versus signal-to-noise ratio is demonstrated for two exemplary block codes. The comparison with a conventional decoding algorithm for a (15 cyclic redundancy code shows for example that the bit error rate at 7dB signal-to-noise ratio can be decreased by two orders of magnitude. 1.

Citation Download Citation

Clark D. Jeffries and Peter Protzel "High-order neural models for error-correcting code", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21202

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available