针对常规SOFM(self-organizing feature map)无监督的神经网络,提出了一种改进的自组织特征映射SOFM神经网络算法.在常规SOFM网络数据聚类算法基础上,分析了其在实际应用中存在的不足,对初始权值设定以及邻域范围选择等方面进行了算法的优化和改进,进而提高了SOFM神经网络聚类算法的正确率、收敛速度和实时性,并利用仿真实验进一步对提出的改进算法进行了验证.%SOFM (self-organizing feature map) algorithm is a clustering method that can cluster on non-supervision condition. An improved algorithm based on SOFM neural network clustering was introduced in this paper. It proposed the basic data clustering theory on SOFM and found problems in applications. The selection method of initial weights and the scope of neighborhood parameters were improved to increase the correct rate, convergence speed and computational efficiency of data clustering. The improved clustering algorithm is verified by simulation results.
展开▼
