纠错码与神经网络

从两个方面推广了Bruck和Blaum的工作。一方面,证明了在软判决译码的情况下,线性分组码的最大似然译码等价于一个神经网络收敛于它能量函数的全局极大状态。另一方面,对GF(p)上的线性分组码,构造了一个联想记忆神经网络,使得每一个线性分组码的码字都对应于神经网络的一个全局稳定状态。

ERROR-CORRECTING CODES AND NEURAL NETWORKS

In this paper, Bruck and Blaum's conclusions are generalized in two aspects. On the one band, the maximum likelihood decoding of linear block codes in the soft-decision condition is proved to be equivalent to making a neural network converge to a globe stable state of the energy function. On the other hand, we construct such an associative memory neural network for a linear block code in GF(p) that each codeword of the code is respctive to a globe maximum point of the energy function of the neural network.