Decode to channel binary block codes based on neural networks and genetic algorithm

In this article, an error-correction decoding technique using Hopfield neur al networks (HNN) and genetic algorithm( GA) is presented. First, based on the elementary relationship between HNN and binary block codes, a new effec tive algorithm is put forward that can perform maximum likelihood decoding to all cyclic block codes ( for example, (23, 12) Golay code sets) by use o f high-order mutual connecting neural networks. The decoding to each receiv ed code word is processed in parallel, and thus it is suitable for high-rat e data communication. Second, this HNN decoding algorithm is combined with the genetic algorithm and a scheme decoding general binary block code is de veloped. The main merit of the proposed HNN and GA decoding technique is th at it can efficiently escape the local minima of the energy function and co mpletely achieve correct decoding for all binary block codes.