The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding, Jiling

doi:10.1155/2017/5292894

Cited by 38 publications

(28 citation statements)

References 52 publications

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“…Parameter estimation is significant in system modeling [14,15]. Multi-input multi-output systems widely exist in industrial control areas, which are also called multivariate systems or multivariable systems [16][17][18]. They are more complex in model structures than single-input single-output systems and always have high dimensions and numerous parameters, which make the parameter estimation more difficult.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input-output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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“…Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency. The proposed IV-OMP optimization method can be extended to the colored noise systems, the networked dynamic systems [42][43][44][45][46], and so on.…”

Section: Discussionmentioning

confidence: 99%

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

2018

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

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“…16 Maximum likelihood estimation techniques are important for parameter estimation and system modeling. 25 The iterative methods are often used in designing the controller 26,27 and finding the solutions of matrix equations or the roots of nonlinear equations. Due to their good statistical properties of consistency, asymptotic normality, and availability, numerous likelihood estimation methods are developed for different models.…”

Section: Introductionmentioning

confidence: 99%

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

Ding

2019

Adaptive Control & Signal

180

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For a special class of nonlinear systems (ie, bilinear systems) with autoregressive moving average noise, this paper gives the input-output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model and the maximum likelihood principle, a filtering-based maximum likelihood hierarchical gradient iterative algorithm and a filtering-based maximum likelihood hierarchical least squares iterative algorithm are developed for identifying the parameters of bilinear systems with colored noises. The original bilinear systems are divided into three subsystems by using the data filtering technique and the hierarchical identification principle, and they are identified respectively. Compared with the gradient-based iterative algorithm and the multi-innovation stochastic gradient algorithm, the proposed algorithms have higher computational efficiency and parameter estimation accuracy. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.

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The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Cited by 38 publications

References 52 publications

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

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