厌氧过程的循环神经网络生物质观测器

2008 IEEE International Symposium on Intelligent Control Pub Date : 2008-09-30 DOI:10.1109/ISIC.2008.4635946

Diana A. Urrego-Patarroyo, E. Sánchez, S. Carlos-Hernandez, J. Béteau

{"title":"厌氧过程的循环神经网络生物质观测器","authors":"Diana A. Urrego-Patarroyo, E. Sánchez, S. Carlos-Hernandez, J. Béteau","doi":"10.1109/ISIC.2008.4635946","DOIUrl":null,"url":null,"abstract":"In this paper, a recurrent neural networks observer for anaerobic processes is proposed; the main objective is to estimate biomass, in a completely stirred tank reactor. The neural network is trained with an extended Kalman filter algorithm. The applicability of the proposed observer is verified via simulations.","PeriodicalId":342070,"journal":{"name":"2008 IEEE International Symposium on Intelligent Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Recurrent Neural Networks Biomass Observer for Anaerobic Processes\",\"authors\":\"Diana A. Urrego-Patarroyo, E. Sánchez, S. Carlos-Hernandez, J. Béteau\",\"doi\":\"10.1109/ISIC.2008.4635946\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a recurrent neural networks observer for anaerobic processes is proposed; the main objective is to estimate biomass, in a completely stirred tank reactor. The neural network is trained with an extended Kalman filter algorithm. The applicability of the proposed observer is verified via simulations.\",\"PeriodicalId\":342070,\"journal\":{\"name\":\"2008 IEEE International Symposium on Intelligent Control\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Intelligent Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2008.4635946\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2008.4635946","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

本文提出了厌氧过程的递归神经网络观测器;主要目的是在完全搅拌的槽式反应器中估计生物量。神经网络采用扩展卡尔曼滤波算法进行训练。通过仿真验证了所提观测器的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Recurrent Neural Networks Biomass Observer for Anaerobic Processes

In this paper, a recurrent neural networks observer for anaerobic processes is proposed; the main objective is to estimate biomass, in a completely stirred tank reactor. The neural network is trained with an extended Kalman filter algorithm. The applicability of the proposed observer is verified via simulations.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2008 IEEE International Symposium on Intelligent Control

自引率

0.00%

发文量