{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:15:42Z","timestamp":1753884942694,"version":"3.41.2"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","funder":[{"name":"Research on the Construction and Implementation Path of High Quality Development Motivation Mechanism for the Sports Industry in Anhui Province","award":["SK2021A0716"],"award-info":[{"award-number":["SK2021A0716"]}]},{"name":"The practical and teaching base of university - enterprise cooperation between Bengbu University and Hequan Small Town of quality engineering project of Anhui Province","award":["2022xqhz043"],"award-info":[{"award-number":["2022xqhz043"]}]},{"name":"The quality development route study of Changjiang Delta area manufacture driven by digital economy of humanities important project of Anhui Provincial Education Dept.","award":["SK2024A0726"],"award-info":[{"award-number":["SK2024A0726"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,5,30]]},"abstract":"<jats:p> Aiming at the problem that the application of Artificial Neural Network (ANN) only stays in the position of biology and single medical treatment, the creation of commercial value is limited, and the general commercial neural network technology is not enough, combined with information set setting construction model, RNN classification model, FLS method, GRU neural network operation method, LP economic trend method, Copula function model, Sol time sense data model, PIO factor model and other methods to optimize the economic benefits generated by ANN. The prediction model in the macroeconomic variable set is constructed by using the information set setting, and the commercial products preferred by the crowd are extracted. At the same time, the analysis and utilization of macroeconomic trends are proposed for the basis of economic and commercial decision-making, so as to better solve the problems of unit economic data status and gating structure, and further rationally arrange neural network resources. The use of multiple models for macroeconomic uncertainty and artificial neural network to determine the relationship between systematic commercial goods provides help, but also make the macro-financial risk indicators of the value of the range of change more obvious. The results of the improvement of the experimental method show that the use of multi-model algorithm makes the positive benefit value of macro-economy more effective, and the ability of artificial neural network technology to bring benefits is stronger. <\/jats:p>","DOI":"10.1142\/s0218126625501580","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T10:13:04Z","timestamp":1733393584000},"source":"Crossref","is-referenced-by-count":0,"title":["Macroeconomic Growth Forecast and Key Index Analysis Based on Artificial Neural Network"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4508-5160","authenticated-orcid":false,"given":"Chao","family":"Fang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Bengbu University, Bengbu, Anhui 233030, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9512-1646","authenticated-orcid":false,"given":"Xue","family":"Jiang","sequence":"additional","affiliation":[{"name":"Graduate School, Semyung University, Jecheon-si, Chungcheongbuk-do 27136, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0726-6468","authenticated-orcid":false,"given":"Bin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Bengbu University, Bengbu, Anhui 233030, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"S0218126625501580BIB001","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2"},{"key":"S0218126625501580BIB002","doi-asserted-by":"publisher","DOI":"10.1145\/376284.375731"},{"key":"S0218126625501580BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/12.106218"},{"key":"S0218126625501580BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90012-T"},{"key":"S0218126625501580BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/0164-0704(94)90084-1"},{"key":"S0218126625501580BIB006","doi-asserted-by":"publisher","DOI":"10.1086\/261737"},{"key":"S0218126625501580BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/S1574-0072(02)10010-7"},{"key":"S0218126625501580BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2016.04.008"},{"key":"S0218126625501580BIB009","first-page":"7","volume":"1","author":"Omid A. 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