学习算法对反馈神经网络故障性能的影响

Effects of the learning algorithm on the faulty behavior of feedback neural networks

以离散 Hopfield神经网络为例 ,详细研究了学习算法对反馈神经网络故障性能的影响。简要介绍了离散Hopfield神经网络及其采用的 Hebb学习算法。详细分析了连接故障在学习过程中对网络连接权值故障性能的影响。给出了网络输出状态概率分布变化的计算公式。进行了计算机仿真 。

This paper studies the faulty behavior of feedback neural networks with stuck at faults during the course of learning using the discrete hopfield neural network (DHNN) as an example. DHNN and its Hebb learning algorithm are briefly introduced. Then the study analyzes the influence of link faults on the weights during learning and gives formulas for output probability distribution verification. A computer simulation verifies the accuracy the theoretical analysis.