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A Theory of Neural Networks for Control

CORDIS · observation · Publication date unknown

A Theory of Neural Networks for Control As neural networks are delivering groundbreaking performance in various machine learning frameworks --- ranging from the basic framework of supervised learning to the powerful and challenging framework of control --- immense efforts focus on developing underlying mathematical theories. Recent years witnessed breakthrough contributions to the theory of neural networks for supervised learning, by myself and others. Yet, from a theoretical perspective, much is left to be elucidated about neural networks in the powerful framework of control, leading to a predominantly heuristic implementation, which hinders their use in control application domains where safety, robustness and reliability are critical, e.g. healthcare, aerospace and manufacturing. The overarching goal of the proposed research is to develop a comprehensive mathematical theory of neural networks for control, providing an explanative formalism for intriguing empirical phenomena, as well as break

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recordType

award

status

SIGNED

region

EU

value

1493750

unit

EUR