{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:14:59Z","timestamp":1753884899749,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T00:00:00Z","timestamp":1636156800000},"content-version":"vor","delay-in-days":309,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019024","name":"Guangdong Polytechnic Normal University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019024","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources has become the key issue of human resource management evaluation. However, the current human resource management evaluation system has some problems, such as poor timeliness, one\u2010sidedness, and subjectivity. Therefore, this paper proposes a BP image neural network optimized based on the simulated annealing algorithm to realize enterprise human resource management evaluation and image analysis. Through the learning of different time series samples, the average weight distribution scheme of main indicators is obtained, in which the average weight proportions of <jats:italic>c<\/jats:italic><jats:sub>1<\/jats:sub>, <jats:italic>c<\/jats:italic><jats:sub>2<\/jats:sub>, <jats:italic>c<\/jats:italic><jats:sub>3<\/jats:sub>, and <jats:italic>c<\/jats:italic><jats:sub>4<\/jats:sub> are 25.5%, 24.8%, 17.9%, and 31.9%, respectively. In the comprehensive evaluation of enterprise employees, the error between the actual output and expected output is less than 4.5%. The results show that the BP image neural network based on simulated annealing algorithm has high accuracy in the image analysis and evaluation of enterprise human resource management. The output analysis results meet the actual needs of the enterprise and the personal development of employees and provide a decision\u2010making scheme for the evaluation of enterprise human resource management.<\/jats:p>","DOI":"10.1155\/2021\/3133065","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T06:00:03Z","timestamp":1636264803000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing\u2010Optimized BP Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0718-1513","authenticated-orcid":false,"given":"Bo","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4194-3075","authenticated-orcid":false,"given":"Yuanlin","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9082-0712","authenticated-orcid":false,"given":"Jun","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,6]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/ted.2021.3077209"},{"key":"e_1_2_9_2_2","article-title":"Research on intelligent motion strategy of picking robot based on LM-BP neural network","volume":"232","author":"Li Z.","year":"2020","journal-title":"Journal of Agricultural Mechanization Research"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"XiuliW. 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Prediction model of employee flow based on semi-markov chain and its application analysis Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) 2020 IEEE Chongqing China 1829\u20131833.","DOI":"10.1109\/ITOEC49072.2020.9141763"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/15567036.2019.1618997"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1738\/1\/012070"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.106916"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1089.2018.16282"},{"key":"e_1_2_9_20_2","first-page":"136","article-title":"Countermeasures for enterprise human resource management reform in the era of big data","volume":"7","author":"Qi X.","year":"2020","journal-title":"Investment and Entrepreneurship"},{"key":"e_1_2_9_21_2","first-page":"65","article-title":"Research on prediction of comprehensive evaluation of students based on BP neural network","volume":"45","author":"Feng X.","year":"2019","journal-title":"Computer and Network"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103330"},{"key":"e_1_2_9_23_2","first-page":"50","article-title":"Research on human resource quality structure identification model based on BP neural network","volume":"39","author":"He J.","year":"2019","journal-title":"Science and Management"},{"key":"e_1_2_9_24_2","unstructured":"AmosB.andYaratsD. 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