{"title":"利用非线性主成分分析和灰狼优化器优化的支持向量机预测农产品物流需求","authors":"Xiaoye Zhou, Meilin Zhu, Xiaoyun Ma, Yu Zhong","doi":"10.1504/ijims.2021.122717","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":39293,"journal":{"name":"International Journal of Internet Manufacturing and Services","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Forecasting agricultural product logistics demand by nonlinear principal component analysis and a support vector machine optimised by the grey wolf optimiser\",\"authors\":\"Xiaoye Zhou, Meilin Zhu, Xiaoyun Ma, Yu Zhong\",\"doi\":\"10.1504/ijims.2021.122717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":39293,\"journal\":{\"name\":\"International Journal of Internet Manufacturing and Services\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Internet Manufacturing and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijims.2021.122717\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Internet Manufacturing and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijims.2021.122717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Forecasting agricultural product logistics demand by nonlinear principal component analysis and a support vector machine optimised by the grey wolf optimiser