| 基于粒子群优化的自组织特征映射神经网络及应用 |
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| 引用本文： | 吕强,俞金寿.基于粒子群优化的自组织特征映射神经网络及应用[J].控制与决策,2005,20(10):1115-1119. |
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| 作者姓名： | 吕强 俞金寿 |
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| 作者单位： | 华东理工大学自动化研究所,上海,200237 |
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| 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.
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| 关 键 词： | 数据挖掘 自组织特征映射 粒子群算法 核函数 聚类 |
| 文章编号： | 1001-0920(2005)10-1115-05 |
| 收稿时间： | 2004-11-03 |
| 修稿时间： | 2005-01-07 |
Self-organizing Feature Map Neural Network Based on Particle Swarm Optimizer and Its Application |
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| LV Qiang,YU Jin-shou.Self-organizing Feature Map Neural Network Based on Particle Swarm Optimizer and Its Application[J].Control and Decision,2005,20(10):1115-1119. |
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| Authors: | LV Qiang YU Jin-shou |
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| Affiliation: | Research Institute of Automation, East China University of Science Technology, Shanghai 200237, China. |
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| Abstract: | 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. |
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| Keywords: | Data mining Self-organizing map Particle swarm optimizer Kernel function Clustering |
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