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• W293875888 abstract "Nonparametric algorithms recovering the nonlinearity in Hammerstein systems are examined. The algorithms are based on ordered measurements and on compactly supported functions. The contribution of the note consists in that the probability density function of the input signal does not need to be strictly bounded from zero but can vanish in a finite number of points. In this setting, the convergence is established for nonlinearities being piecewise-Lipschitz functions. It is also verified that for p times locally differentiable nonlinearities, the algorithms attain the convergence rate O(n −2p/(2p+1)), the best possible nonparametric one. Noteworthy, the rate is not worsened by irregularities of the input probability density function." @default.
• W293875888 created "2016-06-24" @default.
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• W293875888 date "2015-01-01" @default.
• W293875888 modified "2023-09-23" @default.
• W293875888 title "On Hammerstein System Nonlinearity Identification Algorithms Based on Order Statistics and Compactly Supported Functions" @default.
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• W293875888 doi "https://doi.org/10.1007/978-3-319-13881-7_50" @default.
• W293875888 hasPublicationYear "2015" @default.
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