{"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}
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