This paper introduces the method that use feedfoward single layer neural network to realize Hough Transform (HT). The property and resolution of HT was improved by the modified HTs weight Matrix and the output function of Neuron.
介绍了用前向单层神经网络实现离散Hough变换（HT）的方法，并且通过对其权值矩阵的修正以及神经元输出函数的修正，改善了HT的性能，提高了HT的分辨率。
参考来源 - 用改进的前向神经网络实现离散Hough变换 in C·2,447,543篇论文数据，部分数据来源于NoteExpress
然后，对传统的矩阵聚类算法进行优化，改进为权值矩阵聚类算法。
Then, for tradition matrix clustering algorithm carries on the optimization, improved as weight matrix cluster algorithm.
当网络连接权值矩阵的最小特征值大于激活函数导数的倒数时，网络并行收敛。
When the minimal eigenvalue of connection weights matrix is greater than the reciprocal of derivation of its neuron activation function, the network will be convergent in parallel update mode.
利用该法，可同时确定层次各元素相对重要性的排序权值和进行判断矩阵的一致性检验。
The priority weights of hierarchy elements can be determined and the consistence check of comparison matrix can be done by using AGA AHP.
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