一种量子自组织特征映射网络模型及聚类算法

A quantum self-organization feature mapping networks and clustering algorithm

提出一种量子自组织特征映射网络模型及聚类算法。量子神经元的输入和权值均为量子比特,输出为实数,量子自组织特征映射网络由输入层和竞争层组成。首先将聚类样本转换成量子态形式并提交给输入层,完成聚类样本的输入;然后计算样本量子态与相应权值量子态的相似系数,提取聚类样本所隐含的模式特征,并对其进行自组织,在竞争层将聚类结果表现出来。采用量子门更新量子权值,分无监督和有监督两个阶段完成网络的训练。仿真实验结果表明该模型及算法明显优于普通自组织特征映射网络。

A quantum self-organization feature mapping networks model and its clustering algorithm are presented. Both the input and the weight of the model are represented by the quantum bits, and the output of the model is represented by the real number. The model is composed of input layer and competitive layer. Firstly, the samples are transformed into quantum states and transported to the input layer, and then the similar coefficients of quantum states are computed between the input and the weight. Secondly, the competitive layer extracts the implicit pattern characters of the clustering samples and takes self-organization to them, and then output the clustering result. The quantum states of weight are modified by quantum rotation gates. The networks are trained by the algorithm of the unsupervised learning and supervised learning＇together. Finally two simulation experiments demonstrate that the model and algorithm are evidently superior to the general self-organization feature mapping networks.