摘要
本文首先对模糊C-均值聚类作了简要分析和评论,在此基础上将模拟退火机制引入其中,以克服模糊C-均值聚类的局部性和对初始聚类中心的敏感性;然后,采用了基于贴近度和择近原则的模糊识别方法,文中分析了格贴近度的不足之处,并对之进行了改进;最后,详细设计了上述各算法。仿真结果说明,该方法在识别速度和准确率方面都达到了令人满意的效果,为种子的在线检测提供了一种新思路,也拓展了模糊理论的应用范围。
The paper introduces and remarks Fuzzy C-means Clustering firsdy. On the basis of systematic analysis of current algorithms, simulated annealing mechanism is inducted into fuzzy clustering to solve the locality and the sensitiveness of the initial condition of Fuzzy C-means Clustering. Then, this paper proposes the fuzzy discern method based on approximation value and the principle of selecting the near. The deficiency of dose-approximation value is proposed and improved. Finally, the algorithm is designed in detail. Simulation results show that the new method gets a satisfied result both on speed and the correct rate. Thus a new on-line detecting method for the seek is presented. It widens the application of fuzzy theory.
出处
《培训与研究(湖北教育学院学报)》
2005年第5期40-43,共4页
Training and Research-Journal of Hubei College of Education
关键词
模拟退火算法
种子分类
模式识别
模糊C-均值聚类
Simulated Annealing Algorithm
maize seed
Pattern Recognition
Fuzzy C-means Clustering