{"title":"利用前馈神经网络对带有控制参数的非线性动态系统进行机器学习","authors":"Hidetsugu Sakaguchi","doi":"arxiv-2409.07468","DOIUrl":null,"url":null,"abstract":"Several authors have reported that the echo state network reproduces\nbifurcation diagrams of some nonlinear differential equations using the data\nfor a few control parameters. We demonstrate that a simpler feedforward neural\nnetwork can also reproduce the bifurcation diagram of the logistics map and\nsynchronization transition in globally coupled Stuart-Landau equations.","PeriodicalId":501167,"journal":{"name":"arXiv - PHYS - Chaotic Dynamics","volume":"51 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks\",\"authors\":\"Hidetsugu Sakaguchi\",\"doi\":\"arxiv-2409.07468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several authors have reported that the echo state network reproduces\\nbifurcation diagrams of some nonlinear differential equations using the data\\nfor a few control parameters. We demonstrate that a simpler feedforward neural\\nnetwork can also reproduce the bifurcation diagram of the logistics map and\\nsynchronization transition in globally coupled Stuart-Landau equations.\",\"PeriodicalId\":501167,\"journal\":{\"name\":\"arXiv - PHYS - Chaotic Dynamics\",\"volume\":\"51 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Chaotic Dynamics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07468\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Chaotic Dynamics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks
Several authors have reported that the echo state network reproduces
bifurcation diagrams of some nonlinear differential equations using the data
for a few control parameters. We demonstrate that a simpler feedforward neural
network can also reproduce the bifurcation diagram of the logistics map and
synchronization transition in globally coupled Stuart-Landau equations.