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Found 7 papers, 0 papers with code
Kernel-based function learning in dynamic and non stationary environments
This includes the important exploration-exploitation problems where e. g. a set of agents/robots has to monitor an environment to reconstruct a sensorial field and their movements rules are continuously updated on the basis of the acquired knowledge on the field and/or the surrounding environment.
Absolute integrability of Mercer kernels is only sufficient for RKHS stability
Working in continuous-time, it is the purpose of this note to prove that the same result holds also for Mercer kernels.
On the stability test for reproducing kernel Hilbert spaces
Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces where all the evaluation functionals are linear and bounded.
Sliding-mode theory under feedback constraints and the problem of epidemic control
It is only apparently confined to the linear setting and allows also to study an important set of nonlinear models.
COVID-19 epidemic control using short-term lockdowns for collective gain
In this paper we show that the Australian model can be generalized and given a rigorous mathematical analysis, casting strategies of the type short-term pain for collective gain in the context of sliding-mode control, an important branch of nonlinear control theory.
Mathematical foundations of stable RKHSs
Overall, our new results provide novel mathematical foundations of stable RKHSs with impact on stability tests, impulse responses modeling and computational efficiency of regularized schemes for linear system identification.
Kernel absolute summability is only sufficient for RKHS stability
Many of them model unknown impulse responses exploiting the so called Reproducing Kernel Hilbert spaces (RKHSs) that enjoy the notable property of being in one-to-one correspondence with the class of positive semidefinite kernels.
