A comprehensive evaluation of the effectiveness and robustness of statistical methods and artificial intelligence in control applications

全面评估统计方法和人工智能在控制应用中的有效性和鲁棒性

• 批准号: 2891603
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891603
• 关键词: comprehensive; evaluation; effectiveness; robustness; statistical

Artificial intelligence has a wide variety of applications. Novel research attempts to implement AI and general statistical models in a variety of applications, already with many successes. Nevertheless, the great freedom of choice in model design for any implementation complicates optimisation, while the statistical nature of such models can often be deemed not sufficiently robust. This is especially the case in real-life control, where the margin for error and its consequences are of crucial importance in system design. This project proposes to investigate possible ways to design effective statistical control systems for any general control application, exploiting their benefits while minimising their drawbacks in data collection, robustness and usability.

人工智能有着广泛的应用。新颖的研究尝试在各种应用中实施人工智能和通用统计模型，并已取得了许多成功。然而，任何实现的模型设计的巨大选择自由度都会使优化复杂化，而此类模型的统计性质通常被认为不够稳健。在现实控制中尤其如此，其中误差范围及其后果在系统设计中至关重要。该项目建议研究可能的方法，为任何一般控制应用设计有效的统计控制系统，利用其优点，同时最大限度地减少其在数据收集、稳健性和可用性方面的缺点。