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- W2885185560 abstract "Increasingly demanding performance requirements for dynamical systems motivate the adoption of nonlinear and adaptive control techniques. One challenge is the nonlinearity of the resulting closed-loop system complicates verification that the system does satisfy the requirements at all possible operating conditions. This paper presents a data-driven procedure for efficient simulation-based, statistical verification without the reliance upon exhaustive simulations. In contrast to previous work, this approach introduces a method for online estimation of prediction accuracy without the use of external validation sets. This work also develops a novel active sampling algorithm that iteratively selects additional training points in order to maximize the accuracy of the predictions while still limited to a sample budget. Three case studies demonstrate the utility of the new approach and the results show up to a 50% improvement over state-of-the-art techniques." @default.
- W2885185560 created "2018-08-22" @default.
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- W2885185560 date "2018-06-01" @default.
- W2885185560 modified "2023-10-18" @default.
- W2885185560 title "Active Sampling for Closed-Loop Statistical Verification of Uncertain Nonlinear Systems" @default.
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- W2885185560 doi "https://doi.org/10.23919/acc.2018.8431662" @default.
- W2885185560 hasPublicationYear "2018" @default.
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