A probabilistic neural network for designing good codes

Designing good error-correcting codes typically requires searching in search spaces. The vastness of search space precludes the use of brute force techniques such as exhaustive enumeration. The problem of designing codes so that each code repels others (in the sense of hamming distance) fits well in the framework of neural networks. Formulating an energy function to design codes is very difficult and cannot satisfactorily be solved by Hopfield neural network model. To alleviate these problems, a probabilistic neural network model is proposed. The usefulness of the proposed model is investigated with respect to maximal distance codes and constant weight codes. Results of some code parameters that have been designed using the proposed model are presented.