Semi-Parametric Kernel-Based Identification of Wiener Systems

Risuleo, Riccardo Sven; Lindsten, Fredrik; Hjalmarsson, Håkan

doi:10.1109/cdc.2018.8619482

Cited by 4 publications

(5 citation statements)

References 45 publications

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“…The posterior means ( 22) are computed with M = 3000 and B = 100. SP (Semiparametric) The semiparametric method proposed in [36]. The impulse response is modeled using the stable-spline kernel ( 23) and the static nonlinearity is modeled as a combination of the first m Legendre polynomials.…”

Section: Simulationsmentioning

confidence: 99%

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Bayesian nonparametric identification of Wiener systems

2019

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We propose a nonparametric approach for the identification of Wiener systems. We model the impulse response of the linear block and the static nonlinearity using Gaussian processes. The hyperparameters of the Gaussian processes are estimated using an iterative algorithm based on stochastic approximation expectation maximization. In the iterations, we use elliptical slice sampling to approximate the posterior distribution of the impulse response and update the hyperparameter estimates. The same sampling is finally used to sample the posterior distribution and to compute point estimates. We compare the proposed approach with a parametric approach and a semi-parametric approach. In particular, we show that the proposed method has an advantage when a parametric model for the system is not readily available.

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Section: Simulationsmentioning

confidence: 99%

“…The model we propose is an extension of the semiparametric model in [36]. There, the static nonlinearity was modeled using a combination of basis functions with unknown coefficients.…”

Section: Introductionmentioning

confidence: 99%

Bayesian nonparametric identification of Wiener systems

2019

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“…Lamia et al described the Wiener model using the polynomial nonlinear state space (PNLSS) model and developed an output error identi cation method for the nonlinear block [10]. Riccardo et al presented a kernelbased identi cation to estimate parameters of the Wiener system [11]. e impulse response of the linear block and the static nonlinearity were modelled with a Gaussian process and combination of basis functions, respectively.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm

et al. 2020

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A cubic spline approximation-Bayesian composite quantile regression algorithm is proposed to estimate parameters and structure of the Wiener model with internal noise. Firstly, an ARX model with a high order is taken to represent the linear block; meanwhile, the nonlinear block (reversibility) is approximated by a cubic spline function. Then, parameters are estimated by using the Bayesian composite quantile regression algorithm. In order to reduce the computational burden, the Markov Chain Monte Carlo algorithm is introduced to calculate the expectation of parameters’ posterior distribution. To determine the structure order, the Final Output Error and the Akaike Information Criterion are used in the nonlinear block and the linear block, respectively. Finally, a numerical simulation and an industrial case verify the effectiveness of the proposed algorithm.

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“…= max∥c AR − cAR ∥ 2 . Note that since [115] is a semi-parametric method, it does not estimate the ARX model parameters, and thus the PE criteria is not applicable. Note that since [115] is a semi-parametric method, it does not estimate the ARX model parameters and thus the PE criteria is not applicable.…”

Section: Numerical Experiments and Comparisonsmentioning

confidence: 99%

“…Note that since [115] is a semi-parametric method, it does not estimate the ARX model parameters, and thus the PE criteria is not applicable. Note that since [115] is a semi-parametric method, it does not estimate the ARX model parameters and thus the PE criteria is not applicable. In Table 7.2, it is evident that the proposed algorithm surpasses the alternatives in terms of both accuracy and timing in noiseless scenarios.…”

Section: Numerical Experiments and Comparisonsmentioning

confidence: 99%

Fast semi-algebraic clustering for efficient system identification and geometric scene understanding

Ozbay

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I would like to begin by expressing my deepest gratitude to my advisor, Prof. Mario Sznaier. It was a privilege to work under his guidance throughout my PhD, learning not only how to conduct academic research but also how to tackle tough problems and brainstorm innovative solutions, including the occasional "Yet Another Stupid Idea" (YASI) when faced with unexpected challenges. I am particularly grateful for his encouragement to tackle tough problems and teaching me "Problems worthy of attack prove their worth by fighting back.". Additionally, I would like to thank Prof. Octavia Camps for her domain expertise in the computer vision field and her invaluable contributions to my research. It was a pleasure to work under her guidance and in collaboration with her team to learn about interesting problems in computer vision.I am thankful to my committee members, Prof. Taskin Padir and Prof. Rifat Sipahi, for their invaluable guidance and support. Prof. Padir's passion for robotics and his proactive initiatives was very inspiring. His robotics class provided me with extensive knowledge in the field. Additionally, collaborating with Prof. Sipahi in both academic and extracurricular endeavors has been enriching. I appreciate his support and his efforts in nurturing a sense of community among Turkish students at the university.My heartfelt appreciation goes to my family for their unwavering support and encouragement. My parents have been a constant source of strength, guiding me through challenges and celebrating my achievements. I am also grateful to my sister, who has been a faithful companion on this journey, offering a listening ear and unwavering support. It was a precious gift to have somebody by my side who went through the same process. I am grateful for their belief in me, which has been a driving force during difficult times. Special thanks to my sister and my brother-in-law for being like a second family to me in the US.Finally, I am deeply grateful to my fiancée, Ugurcan Ozdemir for literally being right by my side through all the worst and the best days since he entered my life. His support, constant presence, and unshakeable belief in me, especially during moments of doubt, have been invaluable. He has not only been a sounding board for navigating tough decisions but also provided technical support when needed. To my cherished friends who have become like family, especially

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Semi-Parametric Kernel-Based Identification of Wiener Systems

Cited by 4 publications

References 45 publications

Bayesian nonparametric identification of Wiener systems

Bayesian nonparametric identification of Wiener systems

Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm

Fast semi-algebraic clustering for efficient system identification and geometric scene understanding

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