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- W3140304461 abstract "A class of single-input single-output radial basis function networks (RBFN) is considered with both linear andnonlinear tuning parameters, the latter representing the radial basis functions centers. All parameters are assumed to be unknown except a known common input scaling factor. It is shown that the static RBFN hides a dynamic model in theinput variable, in particular the RBFN output coincides with the output of a suitable observable autonomous system oflinear first order differential equations with respect to a scalarvariable if this quantity coincides with the RBFN output. By virtue of this property when the RBFN input is a time function, under regularity assumptions on the RBFN input trajectory, itis constructed a novel continuous time observer yielding global exponential estimates of the radial basis function centers as well as its linear parameters. If the RBFN is the approximation of an arbitrary nonlinear mapping, the parameters estimatesconvergence is shown to be robust with respect to the neural network output approximation error." @default.
- W3140304461 created "2021-04-13" @default.
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- W3140304461 date "2007-07-04" @default.
- W3140304461 modified "2023-09-23" @default.
- W3140304461 title "A continuous-time global estimator of the parameters of a SISO radial basis function network." @default.
- W3140304461 hasPublicationYear "2007" @default.
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