Study of Random Forest to Identify Wiener–Hammerstein System

Shaikh, Abu Hanif; Barbé, Kurt

doi:10.1109/tim.2020.3018840

Cited by 17 publications

(7 citation statements)

References 35 publications

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“…Many nonlinear regression techniques have been widely adopted in recent works, such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, Support-vector Regressor (SVR) and multi-layer perceptron (MLP) [37][38][39]. The MZM non-linearities can be entirely modeled by a memoryless polynomial model.…”

Section: A Mlp Ann-based Dpd For Time-invariant Systemsmentioning

confidence: 99%

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

et al. 2022

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This manuscript proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVM RMS ), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns.

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Section: A Mlp Ann-based Dpd For Time-invariant Systemsmentioning

confidence: 99%

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

et al. 2022

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“…The parameter estimation of some traditional algorithms in nonlinear systems such as Wiener-Hammerstein may have problems of slow convergence speed and low accuracy (Wills et al, 2013;Shaikh and Barbe, 2021;Vanbeylen, 2014;Giordano et al, 2018). Researchers have proposed some improvement schemes for traditional algorithms to effectively improve their defects.…”

Section: Introductionmentioning

confidence: 99%

“…The parameter estimation of some traditional algorithms in nonlinear systems such as Wiener-Hammerstein may have problems of slow convergence speed and low accuracy (Wills et al ., 2013; Shaikh and Barbe, 2021; Vanbeylen, 2014; Giordano et al. , 2018).…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

Chu,

Li,

Jiang

et al. 2023

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PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

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“…In, 16 the identification problem of Wiener–Hammerstein model is addressed when a disturbance affects the input of the nonlinearity, that is, process noise. In, 17 the authors proposed an iterative random forest as an alternative to select the dynamics combinatorially. In this study,

$$ f(.)

…”

Section: Introductionmentioning

confidence: 99%

Wiener–Hammerstein nonlinear system identification using spectral analysis

Brouri

2022

Intl J Robust & Nonlinear

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In this paper, a new approach is developed for the identification of Wiener-Hammerstein model structures. Unlike several other papers, the transfer functions of linear elements are of unknown structure. Indeed, the linear subsystems are not necessarily parametric or FIR. Furthermore, the function of nonlinear block can be nonparametric and noninvertible. The main challenge in identifying a Wiener-Hammerstein system lies in the separation of the dynamics over the input and the output LTI elements. Presently, this issue does not arise, without any conditions being imposed on the frequency band. Then, a two-stage identification method is suggested. Firstly, the system is excited by a set of constant inputs to capture the system nonlinearity. In the second stage, an identification approach based on the spectral analysis and using periodic input signals is developed to determine the linear elements parameters. In the present method, very interesting concepts are used, like the Fourier decomposition, the frequency approach and the spectrum analysis. Simulations demonstrate that the proposed identification method is both effective and efficient.

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Study of Random Forest to Identify Wiener–Hammerstein System

Cited by 17 publications

References 35 publications

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

Wiener–Hammerstein nonlinear system identification using spectral analysis

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