ReLU neural networks approximate transformed constraints in flat systems as unions of polytopes, enabling mixed-integer programming for guaranteed constraint satisfaction in CLF-based and MPC designs for nonlinear systems.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2verdicts
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SPOT localizes fluorescent emitters via optimization to achieve single-frame super-resolution, resolving 30 nm line pairs and outperforming prior algorithms in noisy conditions.
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An ANN-Enhanced Approach for Flatness-Based Constrained Control of Nonlinear Systems
ReLU neural networks approximate transformed constraints in flat systems as unions of polytopes, enabling mixed-integer programming for guaranteed constraint satisfaction in CLF-based and MPC designs for nonlinear systems.
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Single-frame super-resolution via Sparse Point Optimization
SPOT localizes fluorescent emitters via optimization to achieve single-frame super-resolution, resolving 30 nm line pairs and outperforming prior algorithms in noisy conditions.