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应用科学学报 ›› 2005, Vol. 23 ›› Issue (5): 513-516.

• 论文 • 上一篇    下一篇

基于相近关系的粗糙因子神经网络的模式识别方法

肖迪, 胡寿松   

  1. 南京航空航天大学自动化学院, 江苏南京 210016
  • 收稿日期:2004-05-28 修回日期:2004-08-27 出版日期:2005-09-30 发布日期:2005-09-30
  • 作者简介:肖迪(1975-),女,河北大城人,博士生,E-mail:xiaodi_12@sina.com;胡寿松(1937-),男,浙江慈溪人,教授,博导.
  • 基金资助:
    国家自然科学基金(60234010);航空科学基金(02E52025);国防基础科研(K1603060318)资助项目

Model Identification in Rough Factor Neural Network Based on Nearness Relationship

XIAO Di, HU Shou-song   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2004-05-28 Revised:2004-08-27 Online:2005-09-30 Published:2005-09-30

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83

被引次数

3

摘要： 针对经典粗糙集理论中的不可分辨关系对连续属性值中噪声数据缺乏容错性的情况,提出一种基于个体属性值距离的相近关系,定义了相近关系下的粗糙集理论的基本概念.在相近关系的基础上,提出了衡量粗糙隶属度的方法,研究了该隶属度函数的性质,利用该函数作为粗糙因子设计了粗糙因子神经网络,可减小噪声污染的影响,并使网络的收敛速度得到提高.最后,通过对某型歼击机操纵面故障的模式识别的仿真研究验证了文中方法的正确性和有效性.

关键词: 模式识别, 相近关系, 粗糙因子, 粗糙集, 神经网络

Abstract: In this paper, relation of nearness is proposed to replace that of indiscernibility for increased robustness of a decision system against noise.Preliminaries of nearness relationship rough sets are defined and the properties of nearness rough membership function are studied.An architecture of neural network based on nearness relationship rough sets and nearness rough membership functions is described.Neurons in such a network consist of conventional neurons and rough neurons, each neuron having a rough factor.Influence of noise on the network is reduced, and convergence is speeded.An example based on fault identification of an aircraft actuator is presented.Simulation results indicate effectiveness of the proposed method.

Key words: model identification, rough factor, rough sets, nearness relationship, neural network

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