{"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}
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.
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