麻疯树产量的数学预测:多元线性回归与人工神经网络多层感知器模型的比较

C. Gbèmavo
{"title":"麻疯树产量的数学预测:多元线性回归与人工神经网络多层感知器模型的比较","authors":"C. Gbèmavo","doi":"10.16929/ajas/2020.929.248","DOIUrl":null,"url":null,"abstract":"The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Mathematical prediction of the Jatropha curcas L. plant yield: comparing Multiple Linear Regression and Artificial Neural Network Multilayer Perceptron models\",\"authors\":\"C. Gbèmavo\",\"doi\":\"10.16929/ajas/2020.929.248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model\",\"PeriodicalId\":332314,\"journal\":{\"name\":\"African Journal of Applied Statistics\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"African Journal of Applied Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.16929/ajas/2020.929.248\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Applied Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.16929/ajas/2020.929.248","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

利用人工神经网络(ANN)多层感知器(MLP)模型对麻疯树进行产量预测。利用性能指标对所建立模型的预测能力进行多元线性回归(MLR)检验。根据性能指标,采用人工神经网络- mlp模型对麻麻植株产量预测进行了较MLR模型的改进
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mathematical prediction of the Jatropha curcas L. plant yield: comparing Multiple Linear Regression and Artificial Neural Network Multilayer Perceptron models
The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model
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:481959085
Book学术官方微信