使用多层感知器和直觉模糊Z数字进行TER分配的新框架
1Publicity Department, Changsha Normal University, Changsha, Hunan, 410100, China. yangtt821124@163.com.
Scientific reports
|August 26, 2025
概括
这项研究引入了使用直觉模糊Z数和神经网络的员工绩效评估和资源分配的新模型. 它通过有效处理不确定的或不完整的员工数据来增强人才管理.
科学领域:
- 组织心理学和数据科学.
- 人力资源管理和人工智能.
背景情况:
- 传统的员工评估和资源分配方法面临不确定和复杂的绩效数据.
- 有效分配培训和教育资源对于劳动力能力和发展至关重要.
研究的目的:
- 提出一个准确的员工绩效评估和资源分配的新模式.
- 解决传统方法在管理数据不确定性和不准确性的局限性.
主要方法:
- 综合应用直觉模糊Z数来处理性能数据中的不确定性.
- 使用多层感知器网络来预测员工的绩效趋势.
- 使用模糊逻辑来管理会员,非会员和评估中的犹.
主要成果:
- 拟议的模型具有高预测准确性,R平方值为0.9967.
- 实验分析证实了员工资源分配的显著改善.
- 该模型有效处理不确定性和不完整的员工绩效数据.
结论:
- 这种新模式比传统的员工评估和分配框架有了显著的进步.
- 它为组织人才管理和绩效优化提供了灵活可扩展的解决方案.
- 建议进行进一步的研究以减少数据稀疏性并实现实时集成.
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