摘要
采用粒子群优化(PSO)算法优化权重失真指数(LW D I),提出了基于粒子群优化的SOM(PSO-SOM)训练算法.用该算法取代K ohonen提出的启发式训练算法,同时引进核函数,以加强PSO-SOM算法的非线性聚类能力.以某工厂丙烯腈反应器数据为聚类应用研究对象,研究结果表明,与启发式训练算法相比,PSO-SOM算法能够得到较优的聚类,而且该算法实现简单、便于工程应用,对丙烯腈反应器参数调整以及收率监测具有显著的指导作用.
The self-organizing map （SOM） based on particle swarm optimizer （PSO）（called PSO-SOM） training algorithm is presented by using direct optimization of a locally weighted distortion index （LWDI） that is achieved through PSO algorithm. Kohonen＇s heuristic-based training algorithm is replaced by the PSO-SOM algorithm. Moreover, kernel mathod is introduced to strengthen performance of PSO-SOM nonlinear clustering. A real life application of PSO-SOM algorithm in classifying data of acrylonitrile reactor is provided. The experimental results show that this algorithm can obtain better clustering results than heuristic-based training algorithm and be easily applied for projects because of its simpleness.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第10期1115-1119,共5页
Control and Decision
关键词
数据挖掘
自组织特征映射
粒子群算法
核函数
聚类
Data mining
Self-organizing map
Particle swarm optimizer
Kernel function
Clustering
