Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties
| Autor: | Danilo Comminiello, Aurelio Uncini, Michele Scarpiniti, Raffaele Parisi |
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| Rok vydání: | 2014 |
| Předmět: |
least mean square
Cubic spline function excess mean square error nonlinear adaptive filter Upper and lower bounds hammerstein system identification Adaptive filter Least mean squares filter Nonlinear system Spline (mathematics) Control and Systems Engineering Control theory Signal Processing Kernel adaptive filter spline adaptive filter Applied mathematics Computer Vision and Pattern Recognition Electrical and Electronic Engineering Excess mean square error Software Mathematics |
| Zdroj: | Signal Processing. 100:112-123 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2014.01.019 |
| Popis: | In this paper a novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented. The nonlinear function involved in the adaptation process is based on a uniform cubic spline function that can be properly modified during learning. The spline control points are adaptively changed by using gradient-based techniques. This new kind of adaptive function is then applied to the input of a linear adaptive filter and it is used for the identification of Hammerstein-type nonlinear systems. In addition, we derive a simple form of the adaptation algorithm, an upper bound on the choice of the step-size and a lower bound on the excess mean square error in a theoretical manner. Some experimental results are also presented to demonstrate the effectiveness of the proposed method in the identification of high-order nonlinear systems. |
| Databáze: | OpenAIRE |
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