Convex neural network synthesis for robustness in the 1-norm
CoRR(2024)
摘要
With neural networks being used to control safety-critical systems, they
increasingly have to be both accurate (in the sense of matching inputs to
outputs) and robust. However, these two properties are often at odds with each
other and a trade-off has to be navigated. To address this issue, this paper
proposes a method to generate an approximation of a neural network which is
certifiably more robust. Crucially, the method is fully convex and posed as a
semi-definite programme. An application to robustifying model predictive
control is used to demonstrate the results. The aim of this work is to
introduce a method to navigate the neural network robustness/accuracy
trade-off.
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