{"title":"机器学习算法在忆阻器电流-电压特性建模中的应用","authors":"A. Ereshchenko","doi":"10.29003/m1541.mmmsec-2020/133-136","DOIUrl":null,"url":null,"abstract":"The goal of this work is to explore the possibility of using machine learning algorithms for modeling the current-voltage characteristic of a memristor, using modeling of HfO2 and TiO2 based memristors as an example. The possibility of combining several mathematical models based on the predictions of individual models is investigated. The results obtained using the combined model are compared with the predictions of individual models and experimental data.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"APPLICATION OF MACHINE LEARNING ALGORITHMS FOR MODELING THE CURRENT-VOLTAGE CHARACTERISTIC OF A MEMRISTOR\",\"authors\":\"A. Ereshchenko\",\"doi\":\"10.29003/m1541.mmmsec-2020/133-136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this work is to explore the possibility of using machine learning algorithms for modeling the current-voltage characteristic of a memristor, using modeling of HfO2 and TiO2 based memristors as an example. The possibility of combining several mathematical models based on the predictions of individual models is investigated. The results obtained using the combined model are compared with the predictions of individual models and experimental data.\",\"PeriodicalId\":151453,\"journal\":{\"name\":\"Mathematical modeling in materials science of electronic component\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical modeling in materials science of electronic component\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29003/m1541.mmmsec-2020/133-136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical modeling in materials science of electronic component","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29003/m1541.mmmsec-2020/133-136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
APPLICATION OF MACHINE LEARNING ALGORITHMS FOR MODELING THE CURRENT-VOLTAGE CHARACTERISTIC OF A MEMRISTOR
The goal of this work is to explore the possibility of using machine learning algorithms for modeling the current-voltage characteristic of a memristor, using modeling of HfO2 and TiO2 based memristors as an example. The possibility of combining several mathematical models based on the predictions of individual models is investigated. The results obtained using the combined model are compared with the predictions of individual models and experimental data.
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