智能分类系统的软计算技术:一个案例研究

B. Chakraborty, G. Chakraborty
{"title":"智能分类系统的软计算技术:一个案例研究","authors":"B. Chakraborty, G. Chakraborty","doi":"10.1109/SMCIA.1999.782701","DOIUrl":null,"url":null,"abstract":"Soft computing techniques are becoming popular in designing real world industrial applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms etc., to develop hybrid intelligent autonomous systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. Intelligent classification systems are the most well known attempts. In this work a neuro fuzzy feature selector has been designed which is capable of extracting information in the form of fuzzy rules from numeric as well as non-numeric (linguistic) data. Conventional MLP and a variation of it have been used as the neural models and their performance has been compared by simulation with two different data sets. It is found that the proposed variation of the conventional MLP is better in respect to training time and classification accuracy.","PeriodicalId":222278,"journal":{"name":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Soft computing techniques for intelligent classification system: a case study\",\"authors\":\"B. Chakraborty, G. Chakraborty\",\"doi\":\"10.1109/SMCIA.1999.782701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Soft computing techniques are becoming popular in designing real world industrial applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms etc., to develop hybrid intelligent autonomous systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. Intelligent classification systems are the most well known attempts. In this work a neuro fuzzy feature selector has been designed which is capable of extracting information in the form of fuzzy rules from numeric as well as non-numeric (linguistic) data. Conventional MLP and a variation of it have been used as the neural models and their performance has been compared by simulation with two different data sets. It is found that the proposed variation of the conventional MLP is better in respect to training time and classification accuracy.\",\"PeriodicalId\":222278,\"journal\":{\"name\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMCIA.1999.782701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.1999.782701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

软计算技术在设计现实世界的工业应用程序中越来越流行。研究人员正在尝试整合不同的软计算范式,如模糊逻辑、人工神经网络、遗传算法等,开发混合智能自主系统,通过利用现实生活情况的容忍度和不确定性,提供更大的灵活性。智能分类系统是最著名的尝试。在这项工作中,设计了一个神经模糊特征选择器,它能够从数字和非数字(语言)数据中以模糊规则的形式提取信息。采用传统MLP及其变体作为神经网络模型,并在两种不同的数据集上进行了仿真比较。结果表明,该方法在训练时间和分类精度方面优于传统MLP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Soft computing techniques for intelligent classification system: a case study
Soft computing techniques are becoming popular in designing real world industrial applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms etc., to develop hybrid intelligent autonomous systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. Intelligent classification systems are the most well known attempts. In this work a neuro fuzzy feature selector has been designed which is capable of extracting information in the form of fuzzy rules from numeric as well as non-numeric (linguistic) data. Conventional MLP and a variation of it have been used as the neural models and their performance has been compared by simulation with two different data sets. It is found that the proposed variation of the conventional MLP is better in respect to training time and classification accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信