人工神经网络在底栖生物群落物种数量估算中的应用

Antônio Pelli- Neto, C. Hayashi, Giovana Barbosa de Oliveira, P. Pimenta, A. Pelli
{"title":"人工神经网络在底栖生物群落物种数量估算中的应用","authors":"Antônio Pelli- Neto, C. Hayashi, Giovana Barbosa de Oliveira, P. Pimenta, A. Pelli","doi":"10.15406/ijh.2021.05.00279","DOIUrl":null,"url":null,"abstract":"The least squares method has been largely used in several areas, mainly because of its simplicity. It is a widely used knowledge tool. However, the current advances in Information Technology have contributed to the development of decision support systems, in a search for greater reliability of predictions from samples. The use of Information Technology in Limnology is still limited. The main objective of this study is to show the possibility of using Artificial Neural Network in the process of inference of the total number of the rate of biological communities from samples. Our data show that the use of nonparametric inference, along with nonlinear data mapping, may lead to more consistent and efficient results, as the Artificial Neural Networks.","PeriodicalId":14063,"journal":{"name":"International Journal of Hydrology","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of artificial neural networks in estimating the number of species in benthic communities\",\"authors\":\"Antônio Pelli- Neto, C. Hayashi, Giovana Barbosa de Oliveira, P. Pimenta, A. Pelli\",\"doi\":\"10.15406/ijh.2021.05.00279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The least squares method has been largely used in several areas, mainly because of its simplicity. It is a widely used knowledge tool. However, the current advances in Information Technology have contributed to the development of decision support systems, in a search for greater reliability of predictions from samples. The use of Information Technology in Limnology is still limited. The main objective of this study is to show the possibility of using Artificial Neural Network in the process of inference of the total number of the rate of biological communities from samples. Our data show that the use of nonparametric inference, along with nonlinear data mapping, may lead to more consistent and efficient results, as the Artificial Neural Networks.\",\"PeriodicalId\":14063,\"journal\":{\"name\":\"International Journal of Hydrology\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Hydrology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15406/ijh.2021.05.00279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydrology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15406/ijh.2021.05.00279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

最小二乘法在许多领域得到了广泛的应用,主要是因为它的简单性。它是一个广泛使用的知识工具。然而,目前信息技术的进步促进了决策支持系统的发展,以寻求更可靠的样本预测。信息技术在湖沼学中的应用仍然有限。本研究的主要目的是展示利用人工神经网络从样本中推断生物群落总数的可能性。我们的数据表明,使用非参数推理,以及非线性数据映射,可能会导致更一致和有效的结果,作为人工神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of artificial neural networks in estimating the number of species in benthic communities
The least squares method has been largely used in several areas, mainly because of its simplicity. It is a widely used knowledge tool. However, the current advances in Information Technology have contributed to the development of decision support systems, in a search for greater reliability of predictions from samples. The use of Information Technology in Limnology is still limited. The main objective of this study is to show the possibility of using Artificial Neural Network in the process of inference of the total number of the rate of biological communities from samples. Our data show that the use of nonparametric inference, along with nonlinear data mapping, may lead to more consistent and efficient results, as the Artificial Neural Networks.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信