Nonlinear modeling and prediction by successive approximation using radial basis functions

DOI10.1016/0167-2789(94)90018-3zbMath0811.94007OpenAlexW1992288729MaRDI QIDQ1335306

Alan Lapedes, Xiangdong He

Publication date: 1 November 1994

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-2789(94)90018-3

zbMATH Keywords

predictionradial basis functionschaotic time seriessuccessive approximationsnonlinear system modelingmultilayer radial basis networkreal world time series

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Analytic circuit theory (94C05)

Related Items (2)

Fusion prediction based on the attribute clustering network and the radial basis function ⋮ Control of chaotic dynamical systems using radial basis function network approximators

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