基于模糊控制算法的人工智能在企业创新的应用
1School of Business, Macau University of Science and Technology, Macau, 999078, China.
Heliyon
|March 28, 2024
概括
本研究介绍了使用Q-learning和Takagi Sugeno Fuzzy Control (Q-TSFC) 进行企业创新的智能策略. 人工智能方法增强了决策,提高了客户满意度和绩效效率.
科学领域:
- 计算机科学 计算机科学
- 工商管理 工商管理
背景情况:
- 人工智能 (AI) 对企业创新 (EI) 和竞争力越来越重要.
- 动态的市场需要人工智能在具有挑战性的商业环境中进行有效的决策.
研究的目的:
- 提出一个智能策略,将Q-learning和Takagi Sugeno Fuzzy Control (Q-TSFC) 结合起来,以加强企业创新决策.
- 开发一个框架,将适应性学习与模糊逻辑集成在一起,以应对市场不确定性.
主要方法:
- 实施Q学习,通过自适应式学习和探索来最大限度地提高企业利.
- 利用Takagi Sugeno模糊控制 (Q-TSFC) 来处理学习的Q值和语言惯例进行决策.
- 开发了一个决策框架来管理不准确的市场趋势数据.
主要成果:
- 实现了96.5%的客户满意度比率.
- 达到了96%的企业绩效效率.
- 节省了48%的成本,R2为0.83.
结论:
- Q-TSFC算法有效地改善了企业创新的决策.
- 拟议的AI战略提高了客户满意度,性能效率和成本节约.
- 该方法成功地处理了市场趋势中的语言不确定性,以获得更好的业务成果.
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