Fuzzy transfer learning for real-time decision making under uncertainty

该项目的目标是为下一代实时决策建立新的工具。随着数据变得越来越复杂，有意义的决策支持已经需要强大的知识转移能力、对不确定性的强大鲁棒性和实时分析能力。今天的方法正在努力应对这些挑战。将设计的新模式结合了模糊逻辑、迁移学习、强化学习和深度神经网络。这些集成将为未来十年的实时决策解决方案奠定基础，并将推动不确定性下的机器学习。直接应用包括结构健康监测、气候预测和电信维护。

This project’s objective is to build new tools for the next generation of real-time decision making. As the datasphere grows more complex, meaningful decision support already requires a strong capacity for knowledge transfer, substantial robustness to uncertainty, and real-time analytics. Today’s methods are struggling to meet these challenges. The new schema to be devised combines fuzzy logic, transfer learning, reinforcement learning and deep neural networks. These integrations will lay the foundations for real-time decision-making solutions over the next decade and will advance machine learning under uncertainty. Immediate applications include structural health monitoring, climate prediction and telecommunications maintenance.