神经网络与最小加权距离译码

Neural Networks and Minimum Weighted Distance Decoding

本文旨在研究神经网络在最小加权距离译码中的应用.首先以逐步推广的方式介绍了若干神经网络模型及其收敛性质,然后研究了最小加权距离译码的性质与应用,最后证明求任一能量函数的最小值点问题等价于某一(n,k)线性码的最小加权距离译码问题,并提出用波尔兹曼机的退火法来作最小加权距离译码.

Our main goal is to explore the application of neural networks to minimum weighted distance decoding. At the beginning, we review some models and convergence properties of neural networks step by step. Next, we study properties and applications of the minimum weighted distance decoding. Finally, we prove that finding the global minimum point of any energy function is equivalent to minimum weighted distance decoding in some (n, k) linear code, and propose an algorithm which uses annealing method with Boltzmann machine for minimum weighted distance decoding.