{"title":"基于广义回归神经网络的高效忆阻器建模","authors":"Zehra Gulru Cam, S. Cimen, H. Sedef","doi":"10.1109/ISCO.2016.7726914","DOIUrl":null,"url":null,"abstract":"With the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with Generalized Regression Neural Network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as RON, ROFF, thickness of TiO2, and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.","PeriodicalId":320699,"journal":{"name":"2016 10th International Conference on Intelligent Systems and Control (ISCO)","volume":"494 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Generalized regression neural network based efficient memristor modeling\",\"authors\":\"Zehra Gulru Cam, S. Cimen, H. Sedef\",\"doi\":\"10.1109/ISCO.2016.7726914\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with Generalized Regression Neural Network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as RON, ROFF, thickness of TiO2, and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.\",\"PeriodicalId\":320699,\"journal\":{\"name\":\"2016 10th International Conference on Intelligent Systems and Control (ISCO)\",\"volume\":\"494 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 10th International Conference on Intelligent Systems and Control (ISCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCO.2016.7726914\",\"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 10th International Conference on Intelligent Systems and Control (ISCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCO.2016.7726914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generalized regression neural network based efficient memristor modeling
With the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with Generalized Regression Neural Network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as RON, ROFF, thickness of TiO2, and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.
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