Constrained polynomial matrix zonotopes enable algebraically exact set propagation for data-driven reachability analysis of linear and polynomial systems without over-approximation.
Constrained zonotopes: A new tool for set-based estimation and fault detection
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Constrained matrix convex generators bridge data-driven reachability and statistical estimation by providing minimum-volume uncertainty sets that coincide with Gaussian maximum-likelihood ellipsoids and remain tighter than matrix zonotopes for mixed noise.
Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.
citing papers explorer
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Data-Driven Nonconvex Reachability Analysis using Exact Set Propagation
Constrained polynomial matrix zonotopes enable algebraically exact set propagation for data-driven reachability analysis of linear and polynomial systems without over-approximation.
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Bridging Data-Driven Reachability Analysis and Statistical Estimation via Constrained Matrix Convex Generators
Constrained matrix convex generators bridge data-driven reachability and statistical estimation by providing minimum-volume uncertainty sets that coincide with Gaussian maximum-likelihood ellipsoids and remain tighter than matrix zonotopes for mixed noise.
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Data-Driven Reachability Analysis with Optimal Input Design
Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.