Bias Correction-Based Recursive Estimation for Dual-Rate Output-Error Systems with Sampling Noise

Wang, Xuehai; Zhu, Fang; Ding, Feng

doi:10.1007/s00034-020-01378-x

Cited by 9 publications

(8 citation statements)

References 60 publications

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“…The auxiliary model based recursive least squares (AM-RLS) algorithms developed in [34,56] are mainly used for the parameter estimation of dual-rate linear systems. For comparison, this example extends the AM-RLS algorithm to study the parameter estimation of dual-rate bilinear systems.…”

Section: Examplesmentioning

confidence: 99%

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Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

2021

IET Control Theory & Appl

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This paper considers the iterative parameter estimation for a dual‐rate sampled‐data bilinear system with autoregressive moving average noise. Through combining the auxiliary model identification idea with the data filtering technique, this paper derives two filtering auxiliary model gradient‐based iterative algorithms by using two different filters. The key is to construct an auxiliary model for predicting the unavailable outputs, and to transform the dual‐rate bilinear system identification model into two sub‐identification models. Finally, an auxiliary model gradient‐based iterative (AM‐GI) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the dual‐rate sampled‐data bilinear systems, and can generate more accurate parameter estimates and have a higher computational efficiency than the AM‐GI algorithm.

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Section: Examplesmentioning

confidence: 99%

“…• Compared with the AM-RLS algorithms in [34,56], the iterative algorithms proposed in this paper can make full use of the input and output data, and have a better parameter identification accuracy-see Tables 4-7.…”

Section: Examplesmentioning

confidence: 99%

Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

2021

IET Control Theory & Appl

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“…The proposed algorithm in this article can combine other identification algorithms [54][55][56][57][58][59][60][61] to explore new parameter estimation methods of linear, bilinear, and nonlinear stochastic systems with colored noises [62][63][64][65][66][67] and can be applied to industrial application systems. The process of calculating the estimateŝ(t) andb(t) of the weight vector and the bias term b through the MINR-SVM algorithm (43)-( 55) is shown in Table 2.…”

Section: Algorithm 2 the Minr-svm Algorithm Initialmentioning

confidence: 99%

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

Ding

Wang

2021

Intl J Robust & Nonlinear

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The support vector machine has been widely used in binary classification applications because of the simplicity of its implementation. This article proposes an online identification method based on the support vector machine in the field of parameter identification. By substituting the constraint item into the original criterion function to form a new criterion function about the weight vector and the bias item, and then on the basis of the recursive identification methods, the newly established criterion function is minimized by using the Newton search to derive the Newton recursive support vector machine algorithm. Additionally, in order to improve the performance of the Newton recursive support vector machine algorithm, the multi-innovation identification theory is applied to optimize the algorithm, and a multi-innovation Newton recursive support vector machine algorithm is derived. In addition, the convergence and the calculation amount of the proposed algorithms are carefully analyzed in this article.Finally, a numerical simulation example is given to compare the Newton recursive algorithm and the corresponding multi-innovation recursive algorithm. The results show that the algorithms and optimization strategy studied in this article are effective.

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“…31 Wang et al developed a bias corrected algorithm for dual-rate system with measurement and process noise by means of the polynomial transformation technique. 32 This article considers the parameter estimation problem of Wiener nonlinear systems. The nonlinear block is not needed to be invertible as is generally required in pre-existing work.…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al investigated the problem of data fusion for a class of linear dynamic systems with multiple sensors and presented an optimal state estimation algorithm to overcome the difficulty of asychronous, multiscales, multirate data 31 . Wang et al developed a bias corrected algorithm for dual‐rate system with measurement and process noise by means of the polynomial transformation technique 32 …”

Section: Introductionmentioning

confidence: 99%

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Wang

Zhu

Ding

2020

Adaptive Control & Signal

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This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.

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Bias Correction-Based Recursive Estimation for Dual-Rate Output-Error Systems with Sampling Noise

Cited by 9 publications

References 60 publications

Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

Multi‐innovation Newton recursive methods for solving the support vector machine regression problems

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

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