Improving nonlinear modeling capabilities of functional link adaptive filters

DOI10.1016/J.NEUNET.2015.05.002zbMath1394.68279DBLPjournals/nn/ComminielloSSPU15OpenAlexW794398055WikidataQ50583184 ScholiaQ50583184MaRDI QIDQ1669146

Raffaele Parisi, Danilo Comminiello, Michele Scarpiniti, Simone Scardapane, Aurelio Uncini

Publication date: 30 August 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2015.05.002

zbMATH Keywords

online learning algorithmsnonlinear modelingadaptive combination of filtersfunctional links

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (7)

Steady-state performance of an adaptive combined MISO filter using the multichannel affine projection algorithm ⋮ Müntz-Legendre neural network construction for solving delay optimal control problems of fractional order with equality and inequality constraints ⋮ Model equivalence-based identification algorithm for equation-error systems with colored noise ⋮ Data filtering based recursive and iterative least squares algorithms for parameter estimation of multi-input output systems ⋮ An iterative learning algorithm for feedforward neural networks with random weights ⋮ A semi-supervised random vector functional-link network based on the transductive framework ⋮ Interval variable step-size spline adaptive filter for the identification of nonlinear block-oriented system

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