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一种新型模糊-粗神经网络及其在元音识别中的应用 被引量：5

Fuzzy-rough Neural Network and Its Application to Vowel Recognition
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摘要 为度量模糊粗不确定性信息,引入了模糊粗隶属函数.基于模糊粗糙集理论构建了一种新型的模糊-粗神经网络(FRNN),该网络融合了模糊信息和粗糙信息的处理能力.对5个元音字母的语音识别进行测试,结果显示FRNN网络不仅训练速度快,而且分类性能优于BP网络、RBF网络和贝叶斯分类器. To measure the fuzzy-rough uncertainty, a fuzzy-rough membership function is introduced. A FRNN （Fuzzy-rough Neural Network） is designed based on fuzzy-rough set theory. The FRNN integrates the ability to process fuzzy and rough information. The test result of speech recognition for five vowel characters indicates that the FRNN has the merit of quick learning and it has better classification performance than BP network, RBF network and Bayesian classifier.
作者 张东波 王耀南
机构地区 湖南大学电气与信息工程学院
出处 《控制与决策》 EI CSCD 北大核心 2006年第2期221-224,共4页 Control and Decision
基金 国家自然科学基金项目(60375001) 高等学校博士点基金项目(20030532004) 湖南省教育厅科研基金项目(05C093)
关键词 模糊粗糙集 模糊粗隶属函数 模糊-粗神经网络 元音识别 Fuzzy-rough set Fuzzy-rough membership function Fuzzy-rough neural networks Vowel recognition
分类号 TP8 [自动化与计算机技术—检测技术与自动化装置]
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参考文献12

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二级参考文献22

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共引文献49

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二级引证文献12

控制与决策
2006年 第2期

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