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Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems
Uncertainty is an inherent property of any complex system, especially those that integrate physical parts or operate in real environments.
Towards fuzzification of adaptation rules in self-adaptive architectures
In this paper, we present a method that makes it possible to endow an existing self-adaptive architectures with the ability to learn using neural networks, while preserving domain knowledge existing in the logical rules.
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