基于集成 ISA-BP 神经网络的人才队伍建设绩效预测方法

Shusheng Shen, Yansheng Deng
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引用次数: 0

摘要

引言:客观、准确、公正地制定研究有效的人才队伍建设绩效预测方法,是当前新时期高校管理创新和改革发展的需要,也是提高人才队伍科研质量和教学水平的需要:针对当前人才队伍建设绩效预测研究中存在的指标选取原则不合理、体系不完整、方法不严谨等问题。方法:本文以集成学习为框架,提出了一种基于智能优化算法改进神经网络的人才队伍建设绩效预测方法。首先,通过分析当前人才队伍建设绩效预测影响因素选取原则,分析人才队伍建设绩效管理流程,选取人才队伍建设绩效预测影响因素,构建人才队伍建设绩效分析体系;然后,以集成学习为框架,通过内搜索优化算法改进神经网络,构建人才队伍建设绩效预测模型;最后,通过仿真实验分析验证所提方法的有效性和优越性。结果:结果表明,所提方法满足了实时性要求,同时提高了预测精度。结论:本文解决了人才梯队建设绩效预测精度不高、缺乏完善分析体系的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Performance Prediction Method for Talent Team Building Based on Integrated ISA-BP Neural Networks
INTRODCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team.OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building.METHODS:This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified.RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy.CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system.  
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