2010
DOI: 10.1016/j.sigpro.2010.01.004
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An affine projection-based algorithm for identification of nonlinear Hammerstein systems

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Cited by 35 publications
(5 citation statements)
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“…We then have to optimize the following CF (24) For the derivation of the learning rule for the output linear filter, it is possible to obtain (25) For the spline control points, it is possible to obtain (26) where . For the derivation of the learning rule for the first linear filter, we have that (27) The -th component of the vector can be computed, using (23), as follows: (28) In the derivation of (28) we have assumed that , for , in order to avoid the evaluation of non-causal derivatives.…”
Section: Sandwich 2 Safmentioning
confidence: 99%
“…We then have to optimize the following CF (24) For the derivation of the learning rule for the output linear filter, it is possible to obtain (25) For the spline control points, it is possible to obtain (26) where . For the derivation of the learning rule for the first linear filter, we have that (27) The -th component of the vector can be computed, using (23), as follows: (28) In the derivation of (28) we have assumed that , for , in order to avoid the evaluation of non-causal derivatives.…”
Section: Sandwich 2 Safmentioning
confidence: 99%
“…Polynomial Hammerstein model is a special type of nonlinear filters in which a static nonlinear system is followed by a dynamic linear system [13]. The nonlinear system is approximated by a polynomial function.…”
Section: A Hammerstein Modelmentioning
confidence: 99%
“…In a Hammerstein model, there is a static nonlinear block at first and then a dynamic linear block; however in a Wiener model, the order of the blocks will be changed. The Hammerstein model has been used in various fields of engineering and industrial sciences including bioelectric engineering, control engineering and chemical industries (Smith et al, 2007;Sung & Lee, 2004;Umoh & Ogunfunmi, 2010;Vörös, 1997;Wang & Zhang, 2015).…”
Section: Introductionmentioning
confidence: 99%