Robust identification for nonlinear errors-in-variables systems using the EM algorithm

Guo, Feng; Hariprasad, K.; Huang, Biao; Ding, Yongsheng

doi:10.1016/j.jprocont.2017.03.008

Cited by 20 publications

(4 citation statements)

References 36 publications

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“…Recently, Bottegal et al proposed a two-experiment method to identify Wiener systems by using the data acquired from two separate experiments, in which the first experiment estimates the static nonlinearity and the second experiment identifies the linear block based on the estimated nonlinearity [23]. For nonlinear errors-in-variables systems contaminated with outliers, a robust identification approach is presented by means of the expectation maximization under the framework of the maximum likelihood estimation [24,25].…”

Section: Introductionmentioning

confidence: 99%

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

et al. 2019

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It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

et al. 2019

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“…In the work of Pearson, different kinds of outliers, several experimental outlier‐contaminated data records, and some specific algorithms to deal with such noises have been presented. In the literature, a large number of models have been utilized for representing outliers, some of which are non‐Gaussian distributions such as α ‐stable noise, t‐distribution, and amplitude‐modulated binary‐state sequence such as Bernoulli‐Gaussian . Furthermore, for the purpose of outlier detection, outlying measurements has been represented deterministically .…”

Section: Introductionmentioning

confidence: 99%

Robust identification of MISO neuro‐fractional‐order Hammerstein systems

Rahmani

Farrokhi

2019

Intl J Robust & Nonlinear

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Summary This paper introduces a multiple‐input–single‐output (MISO) neuro‐fractional‐order Hammerstein (NFH) model with a Lyapunov‐based identification method, which is robust in the presence of outliers. The proposed model is composed of a multiple‐input–multiple‐output radial basis function neural network in series with a MISO linear fractional‐order system. The state‐space matrices of the NFH are identified in the time domain via the Lyapunov stability theory using input‐output data acquired from the system. In this regard, the need for the system state variables is eliminated by introducing the auxiliary input‐output filtered signals into the identification laws. Moreover, since practical measurement data may contain outliers, which degrade performance of the identification methods (eg, least‐square–based methods), a Gaussian Lyapunov function is proposed, which is rather insensitive to outliers compared with commonly used quadratic Lyapunov function. In addition, stability and convergence analysis of the presented method is provided. Comparative example verifies superior performance of the proposed method as compared with the algorithm based on the quadratic Lyapunov function and a recently developed input‐output regression‐based robust identification algorithm.

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“…The iterative formulas to estimate the model parameter u, scale parameter r and output time-delay t i are provided in equations (30), (32) and (33), respectively. The proposed robust global identification approach for LPV FIR model with slowly sampled outputs is concluded in Algorithm 1.…”

Section: Brief Introduction To Em Algorithmmentioning

confidence: 99%

“…The robust observation models like Laplace distribution model, 24 Student’s t -distribution model 19 and contaminated Gaussian distribution model are all effective to deal with this problem. Guo et al 30 employed the Student’s t -distribution to model the measurement noise and a robust approach for the errors-in-variables system identification was derived under the framework of EM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Linear parameter-varying systems identification with slowly sampled outputs subjected to unknown time-delays and outliers

Liu

Yang

Guo³

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes a robust global identification approach for nonlinear systems with slowly sampled outputs subjected to unknown time-delays. The inputs of the system are fast-rate sampled while the informative outputs are uniformly sampled at a slow rate. In practical industries, the outlier and output time-delays are commonly encountered and they are both considered in this work. To eliminate the impact brought by the outliers, the robust linear parametervarying finite impulse response observation model based on the Laplace distribution is established. The unknown timedelay is treated as a random integer at each sampling moment which follows the uniform distribution with the boundary known a priori. The proposed robust approach is derived in the expectation-maximization algorithm scheme and the formulas to iteratively update the model parameters, the scale parameter and the random output time-delays are derived, respectively. The unmeasurable noise-free fast-rate sampled outputs are estimated by simulating the identified model. A numerical example, the mass-spring-damper system and the two-link robotic manipulator are utilized to verify the effectiveness of the proposed approach.

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Robust identification for nonlinear errors-in-variables systems using the EM algorithm

Cited by 20 publications

References 36 publications

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Robust identification of MISO neuro‐fractional‐order Hammerstein systems

Linear parameter-varying systems identification with slowly sampled outputs subjected to unknown time-delays and outliers

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