7 citations · 27 across the 14 of their papers we have counts for
7 papers · 1 filter
Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter co…
Sufficient Conditions for Parameter Convergence over Embedded Manifolds using Kernel Techniques
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The persistence of excitation (PE) condition is sufficient to ensure parameter convergence in adaptive estimation problems. Recent results on adaptive estimation in reproducing ker…
Approximations of the Reproducing Kernel Hilbert Space (RKHS) Embedding Method over Manifolds
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
The reproducing kernel Hilbert space (RKHS) embedding method is a recently introduced estimation approach that seeks to identify the unknown or uncertain function in the governing…
Intrinsic and Extrinsic Approximation of Koopman Operators over Manifolds
Sai Tej Paruchuri, Jia Guo, Michael Kepler +4
This paper derives rates of convergence of certain approximations of the Koopman operators that are associated with discrete, deterministic, continuous semiflows on a complete metr…
Learning Theory for Estimation of Animal Motion Submanifolds
Nathan Powell, Andrew Kurdila
This paper describes the formulation and experimental testing of a novel method for the estimation and approximation of submanifold models of animal motion. It is assumed that the…
RKHS Embedding for Estimating Nonlinear Piezoelectric Systems
Sai Tej Paruchuri, Jia Guo, Andrew J. Kurdila
Nonlinearities in piezoelectric systems can arise from internal factors such as nonlinear constitutive laws or external factors like realizations of boundary conditions. It can be…
Partial Persistence of Excitation in RKHS Embedded Adaptive Estimation
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
In this paper, an adaptive non-parametric method is proposed to estimate the scalar-valued nonlinear function that appears in uncertain systems governed by ordinary differential eq…