基于Z-Score Pro计算模型和BP神经网络模型的大规模创新类竞赛的评判方案
Zhuoting Yu1, Hongzhong Deng1, Shuaiwen Tang1
1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
这项研究引入了一个新的框架,以优化大规模创新竞赛中的评审. 它通过使用遗传算法,先进的Z分数调整和神经网络来提高公平性和客观性,以获得更可靠的评估.
科学领域:
- 创新管理 创新管理
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 大规模的创新竞赛在评估公平性和客观性方面面临挑战.
- 现有的方法在评估者主观性,工作负载不平衡和得分不确定性方面扎.
研究的目的:
- 开发一个新的框架,以优化大规模创新竞赛中的评审方案.
- 解决评分公平性,准确性,评价者主观性和工作负载不平衡等问题.
主要方法:
- 一个基于遗传算法的工作交叉分布模型,使用信息来平衡评估任务.
- 修改了Z-score和Z-score Pro,以消除法官之间的分数差异.
- 一个BP神经网络用于得分调整,以完善准确性和捕获潜在偏差.
主要成果:
- 拟议的框架大大减轻了来自不同评分趋势的不一致性.
- 提高了标准化过程和评估结果的可靠性.
- 在评估框架中提高了公平性,稳定性和客观性.
结论:
- 综合框架为复杂的判断挑战提供了全面和可扩展的解决方案.
- 这些方法在科学和客观评估中提升了技术水平.
- 优化的评审方案在创新竞赛中带来更可靠的结果.
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