一种粗糙模糊神经网络识别器及其应用

赵晓霞

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一种粗糙模糊神经网络识别器及其应用

更新时间：2026-09-14

- 一种粗糙模糊神经网络识别器及其应用
- 一种粗糙模糊神经网络识别器及其应用
- Issue 3, (2009)
- 作者机构：
  
  中北大学信息与通信工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2009
- 稿件说明：
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赵晓霞. 一种粗糙模糊神经网络识别器及其应用[J]. 2009, (3).
DOI：

赵晓霞. 一种粗糙模糊神经网络识别器及其应用[J]. 2009, (3). DOI：

摘要

结合粗糙集和模糊神经网络提出了一种粗糙模糊神经网络识别器的模型.该模型根据粗糙集理论对训练样本进行建立决策表、离散决策表、约简决策表、提取分类规则等推理过程设计.粗糙模糊神经网络识别器的输入层、两个隐含层、输出层的神经元个数分别根据决策表的约简结果、离散结果和分类规则、决策属性决定.将该识别器用于车牌字符识别

实验表明:该方法比粗糙集规则匹配识别方法识别率提高了18%

比BP神经网络识别方法识别率提高了2.7%.

Abstract

A model of rough fuzzy neural network classifier was presented by combining rough set and fuzzy neural network.The model was designed according to the reasoning process of the rough set theory

including building up

dispersing and reducing the decision table of training samples.In the rough fuzzy neural network classifier

the number of the neurons of the input layer

two hiding layers and the output layer were determined by the reduction results

the dispersal results

classification rules and the decision attribute of the decision table

respectively.The application of the classifier to the car's plate characters recognition shows that it can increase the recognition rate of 18% compared with the method based on rough set rule matching

and 2.7% compared with the method based on BP neural network.

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