An affine projection-based algorithm for identification of nonlinear Hammerstein systems

Umoh, Ifiok J.; Ogunfunmi, Tokunbo

doi:10.1016/j.sigpro.2010.01.004

Cited by 35 publications

(5 citation statements)

References 18 publications

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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%

Novel Cascade Spline Architectures for the Identification of Nonlinear Systems

Scarpiniti

Comminiello

Parisi

et al. 2015

IEEE Trans. Circuits Syst. I

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In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques.\ud In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method

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Section: Sandwich 2 Safmentioning

confidence: 99%

Novel Cascade Spline Architectures for the Identification of Nonlinear Systems

Scarpiniti

Comminiello

Parisi

et al. 2015

IEEE Trans. Circuits Syst. I

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“…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%

Sub and ultra harmonic extraction using several Hammerstein models

Sbeity

Girault

Ménigot

et al. 2012

2012 IEEE International Conference on Complex Systems (ICCS)

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Ultrasound contrast imaging has been introduced in order to increase the contrast of echographic images by injecting micro-bubbles in the vascular system. They are gaz filled microbubbles with nonlinear behavior. One of the most used modality of ultrasound contrast imaging is the second harmonic imaging. This imaging technique, based on the reception of the second harmonic, is devoted to image only the nonlinearity of the microbubble. However, in such ultrasound images the contrast is limited by the nonlinear components of non-perfused tissue. Sub and ultra harmonic imaging appeared to be an interesting alternative to overcome this limitation since, unlike tissue, microbubbles generate sub and ultra harmonics. In order to extract optimally these sub and ultra harmonic components, we proposed a modified Hammerstein model able to model and extract sub and ultra harmonics. Results showed i) that microbubble signal is accurately represented both in time and frequency domains and ii) that sub-and ultra-harmonics were well extracted and separated from harmonic component. Note that the gain achieved by comparing the filtering signals by the modified Hammerstein and the standard Hammerstein was 4.6 dB.

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“…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%