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• W2002561104 abstract "This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined." @default.
• W2002561104 created "2016-06-24" @default.
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• W2002561104 date "2014-07-01" @default.
• W2002561104 modified "2023-10-14" @default.
• W2002561104 title "Explicit Recursive and Adaptive Filtering in Reproducing Kernel Hilbert Spaces" @default.
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• W2002561104 doi "https://doi.org/10.1109/tnnls.2013.2293871" @default.
• W2002561104 hasPublicationYear "2014" @default.
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