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We propose a non-linear recursive solution to the problem of short-term prediction of glucose in type 1 diabetes. The Fixed Budget Quantized Kernel Least Mean Square (QKLMS-FB) algorithm is employed to construct a univariate model of subcutaneous glucose concentration, which: (i) handles nonlinearities by transforming the input space into a high-dimensional Reproducing Kernel Hilbert Space and, (ii)...
The kernel least mean square (KLMS) algorithm is an efficient non-linear adaptive filter that operates in the reproducing kernel Hilbert space (RKHS). In realistic applications of system identification or time series prediction, there are usually multiple inputs that demand multiple kernels or kernel parameters. This paper proposes to use a tensor product kernel for KLMS that accommodates multiple...
Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an...
Correntropy has been successfully applied in non-Gaussian signal processing, but the superior performance achieved is depends on appropriate selection of the kernel width. How to select a proper kernel width is a crucial problem in correntropy applications. In this paper, we propose an adaptive algorithm to update the kernel width, which is set at a maximum between the absolute value of instantaneous...
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