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The Occupation Kernel Method for Nonlinear System Identification
[article]
2021
arXiv
pre-print
This manuscript presents a novel approach to nonlinear system identification leveraging densely defined Liouville operators and a new "kernel" function that represents an integration functional over a reproducing kernel Hilbert space (RKHS) dubbed an occupation kernel. The manuscript thoroughly explores the concept of occupation kernels in the contexts of RKHSs of continuous functions, and establishes Liouville operators over RKHS where several dense domains are found for specific examples of
arXiv:1909.11792v3
fatcat:tnk7sz2izfeo5iusua7xj2smtq