{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:35:55Z","timestamp":1777703755448,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T00:00:00Z","timestamp":1530662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>Under smart city environment, the internet public opinion management is more difficult, therefore the combined prediction model based on grey system theory and fuzzy neural network is constructed. Firstly, the internet public opinion characteristics under smart city is discussed. Secondly, the mathematical model of the grey system theory is studied. the basic structure and mathematical model of fuzzy neural network are analyzed, and then the training algorithm is designed. Finally, simulation analysis of internet public opinion is carried out, simulation results show that the new method can improve prediction correctness of internet public opinion effectively, and the internet public opinion controlling level can be improved.<\/jats:p>","DOI":"10.3233\/jifs-169591","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T11:59:33Z","timestamp":1530878373000},"page":"325-332","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["Research on prediction of internet public opinion based on grey system theory and fuzzy neural network"],"prefix":"10.1177","volume":"35","author":[{"given":"H.","family":"He","sequence":"first","affiliation":[{"name":"School of Network Communication, University of Zhejiang Yuexiu Foreign Languages, Zhejiang, China"}]}],"member":"179","published-online":{"date-parts":[[2018,7,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41324-017-0092-8"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-016-9673-7"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.4156\/jcit.vol8.issue11.71"},{"key":"e_1_3_1_5_2","first-page":"128","article-title":"Prediction of traffic volume in bridge random load process based on grey markov chain","volume":"27","author":"Jiang L.Z.","year":"2010","unstructured":"JiangL.Z., LiC.D., TangB. and ZhuH.B., Prediction of traffic volume in bridge random load process based on grey markov chain, Journal of Highway and Transportation Research and Development27 (2010), 128\u2013132.","journal-title":"Journal of Highway and Transportation Research and Development"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-011-9283-z"},{"key":"e_1_3_1_7_2","first-page":"54","article-title":"Transformer fault diagnosis based on BP neural network optimized by fuzzy clustering and LM algorithm","volume":"49","author":"Song Z.J.","year":"2013","unstructured":"SongZ.J. and WangJ., Transformer fault diagnosis based on BP neural network optimized by fuzzy clustering and LM algorithm, High Voltage Apparatus49 (2013), 54\u201359.","journal-title":"High Voltage Apparatus"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.4304\/jnw.8.9.2021-2028"},{"key":"e_1_3_1_9_2","first-page":"1020","article-title":"Study on prediction of network security situation based on fuzzy neutral network","volume":"6","author":"Wang Y.","year":"2014","unstructured":"WangY. and HuY.T., Study on prediction of network security situation based on fuzzy neutral network, Journal of Chemical and Pharmaceutical Research6 (2014), 1020\u20131026.","journal-title":"Journal of Chemical and Pharmaceutical Research"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169001"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-151753"},{"key":"e_1_3_1_12_2","first-page":"139","article-title":"Structure parameters optimization of explosively formed penetrator based on grey system theory","volume":"29","author":"Liu J.F.","year":"2017","unstructured":"LiuJ.F., LongY., JiC. and XiangD., Structure parameters optimization of explosively formed penetrator based on grey system theory, Journal of Grey System29 (2017), 139\u2013154.","journal-title":"Journal of Grey System"},{"key":"e_1_3_1_13_2","first-page":"79","article-title":"Grey system theory supported markowitz portfolio optimization during high volatility periods","volume":"28","author":"Nurgul C.","year":"2016","unstructured":"NurgulC., CoskunH. and FatihB.M., Grey system theory supported markowitz portfolio optimization during high volatility periods, Journal of Grey System28 (2016), 79\u201395.","journal-title":"Journal of Grey System"}],"container-title":["Journal of Intelligent &amp; 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