系统仿真学报 ›› 2015, Vol. 27 ›› Issue (5): 1105-1111.

• 仿真技术应用 • 上一篇    下一篇

基于Bloch球面旋转的量子自组织网络聚类算法

杨淑云1, 李盼池2   

  1. 1.东北石油大学, 大庆 163318;
    2.东北石油大学计算机与信息技术学院, 大庆 163318
  • 收稿日期:2014-05-05 修回日期:2014-07-20 出版日期:2015-05-08 发布日期:2020-09-01
  • 作者简介:杨淑云(1968-),女,黑龙江安达人,讲师.研究方向为神经网络和智能优化算法。
  • 基金资助:
    国家自然科学基金资助项目(61170132);黑龙江省教育厅科学技术研究项目(12541059);东北石油大学校青年基金资助项目(2013NQ119)

Clustering Algorithm of Quantum Self-Organization Network Based on Bloch Spherical Rotation

Yang Shuyun1, Li Panchi2   

  1. 1. Northeast Petroleum University, Daqing 163318, China;
    2. School of Computer & Information Technology, Northeast Petroleum University, Daqing 163318, China
  • Received:2014-05-05 Revised:2014-07-20 Online:2015-05-08 Published:2020-09-01

摘要: 为提高自组织网络的聚类能力,提出一种基于Bloch球面旋转的量子自组织网络聚类算法。通过使样本数据作为量子比特相位,将样本映射为Bloch球面上的量子比特,将竞争层权值映射为Bloch球面上随机分布的量子比特; 通过计算样本和权值的球面距离最小值,确定获胜节点;通过使获胜节点及其邻域节点在Bloch球面上向着样本旋转来调整这些权值,直到算法收敛。该方法的明显优势在于有较高的聚类精度。以鸢尾属植物样本聚类为例,实验结果表明,提出的方法明显优于传统自组织网络、K-均值聚类等算法。

关键词: 量子比特, Bloch球面旋转, 自组织网络, 聚类算法

Abstract: To enhance the clustering ability of self-origanization network, a quantum-inspired self-organization clustering algorithm was proposed based on Bloch spherical rotation. The clustering samples were mapped to the qubits on the Bloch sphere by taking all the sample values as the phases of the qubits, and the all weight values in the competitive layer were mapped to the qubits randomly distributed on the Bloch sphere. Then, the winning node was obtained by computing the spherical distance between sample and weight value, and the weight values of the winning nodes and its neighborhood were updated by rotating them to the sample on the Bloch sphere until the convergence. The obvious advantage of this method is that it has higher clustering accuracy. The clustering results of the benchmark IRIS sample show that the proposed approach is obviously superior to the classical self-organization network and the K-mean clustering algorithm.

Key words: quantum bits, Bloch spherical rotation, self-organization network, clustering algorithm

中图分类号: