Lin Gao, Pan Zhao, Lin Wang, Haidong Gao, Yaokui Gao, Ming Liu
{"title":"一种新的递归神经网络用于具有惯性和延迟的动态过程建模","authors":"Lin Gao, Pan Zhao, Lin Wang, Haidong Gao, Yaokui Gao, Ming Liu","doi":"10.1109/DTPI55838.2022.9998948","DOIUrl":null,"url":null,"abstract":"A novel continuous-time multi-layer Recurrent Neural Network (RNN) is presented in this paper. The proposed RNN struture has superiorities of simple structure and parameters with certain physical meanings. A four-neuron dynamic neural network was used to model the water spray desuperheating control system. The test results show that the proposed RNN sturture has good adaptability to the physical process with large inertia and large delay effects.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"34 19","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Recurrent Neural Network for Dynamic Process Modeling with Inertia and Delay\",\"authors\":\"Lin Gao, Pan Zhao, Lin Wang, Haidong Gao, Yaokui Gao, Ming Liu\",\"doi\":\"10.1109/DTPI55838.2022.9998948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel continuous-time multi-layer Recurrent Neural Network (RNN) is presented in this paper. The proposed RNN struture has superiorities of simple structure and parameters with certain physical meanings. A four-neuron dynamic neural network was used to model the water spray desuperheating control system. The test results show that the proposed RNN sturture has good adaptability to the physical process with large inertia and large delay effects.\",\"PeriodicalId\":409822,\"journal\":{\"name\":\"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)\",\"volume\":\"34 19\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DTPI55838.2022.9998948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DTPI55838.2022.9998948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Recurrent Neural Network for Dynamic Process Modeling with Inertia and Delay
A novel continuous-time multi-layer Recurrent Neural Network (RNN) is presented in this paper. The proposed RNN struture has superiorities of simple structure and parameters with certain physical meanings. A four-neuron dynamic neural network was used to model the water spray desuperheating control system. The test results show that the proposed RNN sturture has good adaptability to the physical process with large inertia and large delay effects.