摘要
针对模糊核聚类对初始值敏感、易陷入局部最优的缺点,提出了基于粒子群优化的模糊核聚类方法.该方法根据聚类准则设计适应度函数,利用粒子群优化算法对聚类中心进行优化,在迭代优化过程中设计了梯度下降法加快算法的收敛速度,并引入变异机制增强粒子群的多样性.仿真实验及在水轮机转轮叶片裂纹源定位中的应用验证了算法的可行性和有效性.
To overcome the drawbacks of fuzzy kernel clustering method sensitive to the initial cluster centers and easy to be trapped into local minima, the fuzzy kernel clustering method based on particle swarm optimization was proposed. The proposed method designs the fitness function according to the clustering principle and utilizes the particle swarm optimization to optimize the cluster centers. During the procedure of the iterations, the gradient descent operator is designed to accelerate the convergent speed and the mutation operator to improve the diversity of particle swarm. The simulation experiments and the crack source location in turbine blades verify the feasibility and effectiveness of the proposed method.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2009年第6期935-939,共5页
Journal of Shanghai Jiaotong University
关键词
聚类分析
模糊核聚类
粒子群优化算法
梯度下降法
cluster analysis
fuzzy kernel clustering
particle swarm optimization
gradient descent
