基于Fminsearch优化的模型降阶

Shilpi Lavania, D. Nagaria
{"title":"基于Fminsearch优化的模型降阶","authors":"Shilpi Lavania, D. Nagaria","doi":"10.1109/CICT.2016.118","DOIUrl":null,"url":null,"abstract":"A hybrid approach for model order reduction is proposed in this paper. The approximants for denominator polynomial are derived by matching both Markov parameters and Time moments, whereas numerator polynomial derivation and error minimization is done using Fminsearch algorithm. The efficiency of the proposed method can be investigated in terms of closeness of the response of reduced order model with respect to that of higher order original model and a comparison of the integral square error as well.","PeriodicalId":118509,"journal":{"name":"2016 Second International Conference on Computational Intelligence & Communication Technology (CICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Fminsearch Optimization Based Model Order Reduction\",\"authors\":\"Shilpi Lavania, D. Nagaria\",\"doi\":\"10.1109/CICT.2016.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hybrid approach for model order reduction is proposed in this paper. The approximants for denominator polynomial are derived by matching both Markov parameters and Time moments, whereas numerator polynomial derivation and error minimization is done using Fminsearch algorithm. The efficiency of the proposed method can be investigated in terms of closeness of the response of reduced order model with respect to that of higher order original model and a comparison of the integral square error as well.\",\"PeriodicalId\":118509,\"journal\":{\"name\":\"2016 Second International Conference on Computational Intelligence & Communication Technology (CICT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Second International Conference on Computational Intelligence & Communication Technology (CICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICT.2016.118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Second International Conference on Computational Intelligence & Communication Technology (CICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICT.2016.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

提出了一种模型降阶的混合方法。通过匹配马尔可夫参数和时间矩来获得分母多项式的近似值,而使用Fminsearch算法进行分子多项式的求导和误差最小化。该方法的有效性可以从降阶模型的响应与高阶原始模型的响应的接近程度和积分平方误差的比较两方面来考察。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fminsearch Optimization Based Model Order Reduction
A hybrid approach for model order reduction is proposed in this paper. The approximants for denominator polynomial are derived by matching both Markov parameters and Time moments, whereas numerator polynomial derivation and error minimization is done using Fminsearch algorithm. The efficiency of the proposed method can be investigated in terms of closeness of the response of reduced order model with respect to that of higher order original model and a comparison of the integral square error as well.
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:481959085
Book学术官方微信