江苏省高技术产业人才需求预测研究——基于改进的新陈代谢GM(1,1)模型 |
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引用本文: | 胡峰,陆丽娜,黄斌,周文魁.江苏省高技术产业人才需求预测研究——基于改进的新陈代谢GM(1,1)模型[J].科技管理研究,2018(16). |
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作者姓名: | 胡峰 陆丽娜 黄斌 周文魁 |
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作者单位: | 江苏省科学技术发展战略研究院;南京理工大学 |
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基金项目: | 江苏省软科学研究项目“江苏战略性新兴产业国际顶尖科研人才分布研究”(项目编号:BR2017031);江苏省社科应用研究精品工程项目“江苏战略性新兴产业人才需求预测与开发研究”(项目编号:17SRB-15) |
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摘 要: | 利用改进的新陈代谢GM(1,1)模型,借助MATLAB对江苏省高技术产业2016—2020年的人才总量进行灰色预测,并与通过模型预测出的广东、浙江省的高技术产业的人才总量进行对比。建模结果表明,改进的新陈代谢GM(1,1)模型的预测精度比常规模型提高了将近50%,也比新陈代谢GM(1,1)模型和背景值优化模型精度高。预测结果表明,"十三五"末江苏省高技术产业人才总量约为2 549 424人,位于广东之后;人才年均增速约为0.5%,位于浙江、广东之后。
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关 键 词: | 人才需求预测 新陈代谢GM(1,1)模型 背景值优化 高技术产业 |
收稿时间: | 2017/10/26 0:00:00 |
修稿时间: | 2018/1/2 0:00:00 |
Research on Talent Demand Forecast of High-tech Industry in Jiangsu Province :Based on the Improved Metabolic GM (1,1) Model |
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Abstract: | Using the improved metabolic GM (1,1) model, the paper forecasted the total talent of Jiangsu high-tech industry from 2016 to 2020 by means of MATLAB, and compared with the total talent of high-tech industry in Guangdong and Zhejiang which were predicted by model. The modeling results show that the improved metabolic GM (1,1) model has a better prediction accuracy than the conventional model by nearly 50%, and is more accurate than the metabolic GM (1,1) model and the background value optimization model. Tthe forecast results show that, to the end of "thirteen five plan", high-tech industry demand for talent in Jiangsu will reach 2549424, which is ranked behind Guangdong, and the average annual growth rate will amount to 0.5%, which is ranked behind Zhejiang and Guangdong. |
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Keywords: | talent demand forecast metabolic GM (1 1) model background value optimization high-tech industry |
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