Research on Fuzzy Neural Network Modeling Method Based on PSO Clustering Algorithm

WANG Liang-jun; LI Guo-ning; LIU Yu-jia

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Research on Fuzzy Neural Network Modeling Method Based on PSO Clustering Algorithm

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- Research on Fuzzy Neural Network Modeling Method Based on PSO Clustering Algorithm
- Vol. 33, Issue 2, Pages: 54-57(2016)
- 作者机构：
  
  兰州交通大学机构自动化与电气工程学院,甘肃,兰州,730070
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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王亮军, 李国宁, 刘雨佳. 基于粒子群聚类算法的模糊神经网络建模方法研究[J]. 微电子学与计算机, 2016, 33(2): 54-57, 63.

WANG Liang-jun, LI Guo-ning, LIU Yu-jia. Research on Fuzzy Neural Network Modeling Method Based on PSO Clustering Algorithm[J]. 2016, 33(2): 54-57.
王亮军, 李国宁, 刘雨佳. 基于粒子群聚类算法的模糊神经网络建模方法研究[J]. 微电子学与计算机, 2016, 33(2): 54-57, 63. DOI：

WANG Liang-jun, LI Guo-ning, LIU Yu-jia. Research on Fuzzy Neural Network Modeling Method Based on PSO Clustering Algorithm[J]. 2016, 33(2): 54-57. DOI：

摘要

针对复杂系统建模过程中出现的输入输出维数高和规则提取困难等问题

引入模式辨识理论体系中的聚类分析思想

提出了基于粒子群聚类提取样本数据模糊规则的方法.利用粒子群聚类自适应地分析样本聚类中心和聚类数

获得模糊推理规则和隶属度函数个数

结合该方法的特点

建立了一种基于粒子群聚类的模糊神经网络结构.采用模糊RBF算法进行网络训练

调整隶属度函数参数和连接权值

完成网络参数辨识.仿真实例表明

该方法适合复杂系统的建模

具有辨识精度高、收敛速度快和规则自提取的优点

对系统建模具有一定指导意义.

Abstract

Aim at the high dimensionality and rules extracting difficult issues of complex systems modeling

the theory of clustering analysis idea of pattern recognition system be introduced

proposes a method to extract fuzzy rules of the sample data based on POS Clustering algorithm.By analyzing sample data

the paper classify its clustering number and adjust class center

combined with the characteristics of the method

we establish a fuzzy neural network structure based on POS clustering algorithm. The fuzzy RBF network training algorithm used to adjust the membership function parameters and connection weights to complete the network parameter identification. The simulation shows that the method is suitable for the modeling of complex systems

and it has advantages of the high identification accuracy

fast convergence and rules self-extracting.

关键词

Keywords

references

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Leng G, Prasad G, McGinnity T M. An on-line algorithm for creating self-organizing fuzzy neural networks[J]. Neural Networks, 2004, 17(10): 1477-1493. DOI: 1477

Abe S. Neural networks and fuzzy systems[J]. Journal of the Acoustical Society of America, 1992, 12(6): 49-71. DOI: 49

Zhi-Ping P. A learning algorithm for fuzzy neural network based on NFCS form[J]. Journal of Computer Research and Development, 2002, 39(11): 1436-1441. DOI: 1436

Košmelj K, Batagelj V. Cross-sectional approach for clustering time varying data[J]. Journal of Classification, 1990, 7(1): 99-109. DOI: 99

罗勇, 向奕雪. 基于改进PSO算法的模糊神经网络研究[J]. 信息通信, 2014(11): 9-18. DOI: 9

沈艳, 余冬华, 王昊雷. 粒子群K-means聚类算法的改进[J]. Computer Engineering and Applications, 2014, 50(21): 125-128. DOI: 125

贺素良. 遗传算法及模糊神经网络融合技术的研究[J]. 计算机工程, 2003, 29(7): 17-19. DOI: 17

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