基于GBP-Bagging网络模型的产业人力资源需求预测研究——以山西省战略性新兴产业为例

Research on industry human resource demand forecast based on GBP-Bagging network model

摘要:

从资源匹配视角构建战略性新兴产业人力资源需求数量预测指标体系,运用灰色关联度分析筛选关键指标；运用灰色理论、神经网络和集成学习,构建GBP-Bagging网络模型,对山西省2015-2020年战略性新兴产业人力资源需求数量进行预测。结果表明：“十三五”期间,山西省战略性新兴产业人力资源需求数稳步增长；GBP-Bagging网络模型有效克服了单个BP神经网络在小规模数据集上的局限性,获得了比BP神经网络和灰色预测模型更为准确的预测结果；为应对产业人力资源需求增长所带来的压力,山西省应当加大人才培养力度,健全人才引进机制,在增加人才数量的同时,把控人才质量关,加强与周边地区的人才交流与沟通。

Abstract:

Abstract: from the perspective of the construction of index system, prediction of the number of emerging industries of strategic human resource demand from resources, by screening key indicators of grey relational analysis; grey theory, neural network and ensemble learning, constructing GBP-Bagging network model to forecast the demand quantity 2015-2020 of emerging industries of strategic human resources in Shanxi province. The results show that the "13th Five-Year" period, Shanxi province human resources requirements of strategic emerging industries the number of steady growth; GBP-Bagging network model effectively overcomes the limitations of single BP neural network in small scale data set, obtained more accurate prediction results than BP neural network and grey prediction model; brought to deal with the human resources industry the growth in demand pressure, Shanxi province should intensify the cultivation of talents, improve the mechanism of introducing talents, increase in the number of talents at the same time, to control the quality of talent, strengthen personnel exchanges and communication with the surrounding area.