{"title":"Winsorized树突状神经元模型人工神经网络和基于粒子群优化的Tukey双权损失函数鲁棒训练算法","authors":"E. Eğrioğlu, E. Baş, Ozlem Karahasan","doi":"10.1007/s41066-022-00345-y","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":29966,"journal":{"name":"Granular Computing","volume":"10 1 1","pages":"491-501"},"PeriodicalIF":5.5000,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Winsorized dendritic neuron model artificial neural network and a robust training algorithm with Tukey’s biweight loss function based on particle swarm optimization\",\"authors\":\"E. Eğrioğlu, E. Baş, Ozlem Karahasan\",\"doi\":\"10.1007/s41066-022-00345-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":29966,\"journal\":{\"name\":\"Granular Computing\",\"volume\":\"10 1 1\",\"pages\":\"491-501\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2022-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Granular Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s41066-022-00345-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41066-022-00345-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Winsorized dendritic neuron model artificial neural network and a robust training algorithm with Tukey’s biweight loss function based on particle swarm optimization
期刊介绍:
Granular Computing constitutes an extensive body of knowledge, which dwells upon individual formalisms of information granules (established within various settings including set theory, interval calculus, fuzzy sets, rough sets, shadowed sets, probabilistic granules) and unifies them to form a coherent methodological and developmental environment. Granular Computing is about formation, processing and communicating information granules.