{"title":"能够为迟滞油藏计算生成输出序列","authors":"Tsukasa Saito, K. Jin'no","doi":"10.1109/ISOCC53507.2021.9614006","DOIUrl":null,"url":null,"abstract":"Hysteresis reservoir computing generates a variety of output series by changing the parameters of the elements. In this paper, we show that hysteresis reservoir computing has improved its learning ability by changing the parameters, and can represent specific series data.","PeriodicalId":185992,"journal":{"name":"2021 18th International SoC Design Conference (ISOCC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ability to generate output series for Hysteresis Reservoir Computing\",\"authors\":\"Tsukasa Saito, K. Jin'no\",\"doi\":\"10.1109/ISOCC53507.2021.9614006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hysteresis reservoir computing generates a variety of output series by changing the parameters of the elements. In this paper, we show that hysteresis reservoir computing has improved its learning ability by changing the parameters, and can represent specific series data.\",\"PeriodicalId\":185992,\"journal\":{\"name\":\"2021 18th International SoC Design Conference (ISOCC)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 18th International SoC Design Conference (ISOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISOCC53507.2021.9614006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 18th International SoC Design Conference (ISOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISOCC53507.2021.9614006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ability to generate output series for Hysteresis Reservoir Computing
Hysteresis reservoir computing generates a variety of output series by changing the parameters of the elements. In this paper, we show that hysteresis reservoir computing has improved its learning ability by changing the parameters, and can represent specific series data.
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