Iterative estimation for a non-linear IIR filter with moving average noise by means of the data filtering technique

Wang, Yanjiao; Ding, Feng

doi:10.1093/imamci/dnv067

Cited by 38 publications

(20 citation statements)

References 25 publications

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“…In our future work, based on the proposed identification algorithm, attempts will be made to the fault diagnosis control [32][33][34][35][36] and model predictive control [37][38][39][40][41] for PMSMs. The proposed algorithm in this paper can be extended to study identification problems of other linear systems [42,43] and nonlinear systems with colored noise [44][45][46][47]. …”

Section: Discussionmentioning

confidence: 99%

Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

Shi

Wang

2016

Journal of the Franklin Institute

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Section: Discussionmentioning

confidence: 99%

Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

Shi

Wang

2016

Journal of the Franklin Institute

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“…In our future work, based on fuzzy model theory, attempts will be made at adaptive control [36,37,41], state and output feedback control [13,22,25,39], and filter control [26,27] for Hammerstein CAR systems with backlash. The proposed methods can be extended to study identification problems of other nonlinear systems [7,47,48].…”

Section: Discussionmentioning

confidence: 99%

A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

Shi

Wang

2016

Circuits Syst Signal Process

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This study addresses the identification of Hammerstein CAR systems with backlash, where the nonlinear backlash is described as one regression identification model using a two switching function mathematical model. In such a case, the Hammerstein CAR systems with backlash can be transformed into a piecewise linearized model. Then, a novel multi-innovation recursive least squares algorithm with a forgetting factor is applied to estimate the parameters of the proposed model. Finally, numerical examples are presented to test the performance of the proposed algorithm.

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“…Remark 4: By means of the data filtering technique, the F-HGI algorithm updates the estimatesα k ,θ k andθ n,k using (30), (31) and (37), respectively. For the F-HGI algorithm, the block diagram of the filtering process is given in Fig.…”

Section: Remarkmentioning

confidence: 99%

“…The filtering technique can extract the useful information from noisy measurement data for parameter estimation [23,24] and has been used in signal processing and communication [25][26][27], and neural network [28,29]. The filtering technique can reduce the computational burden of some identification algorithms and improve the parameter estimation accuracy [30,31]. In this aspect, Ahirwal et al [32] gave the adaptive filtering of electroencephalogram and event related potential through bounded range artificial bee colony algorithm; Wang and Tang [33,34] applied the filtering technique to a class of non-linear systems and derived several gradient-based iterative estimation algorithms, which can improve parameter estimation accuracy; Wang and Ding [35] presented an input-output data filtering-based recursive least squares parameter estimation for equation-error autoregressive (AR) MA (ARMA) systems.…”

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confidence: 99%

Filtering‐based iterative identification for multivariable systems

Wang

Ding

2016

IET Control Theory & Applications

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104

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This study applies the filtering technique to system identification to study the data filtering-based parameter estimation methods for multivariable systems, which are corrupted by correlated noise -an autoregressive moving average process. To solve the difficulty that the identification model contains the unmeasurable variables and noise terms in the information matrix, the authors present a hierarchical gradient-based iterative (HGI) algorithm by using the hierarchical identification principle. To improve the convergence rate, they apply the filtering technique to derive a filtering-based HGI algorithm and a filtering-based hierarchical least squares-based iterative (HLSI) algorithm. The simulation examples indicate that the filtering-based HLSI algorithm has the highest computational efficiency among these three algorithms. 1 Introduction System modelling is the foundation of control [1, 2], and multivariable systems exist widely in process industries [3], such as the distillation column [4], the internal combustion engine [5] and the industrial wastewater treatment [6]. Parameter estimation has become a hot issue [7, 8], especially in system control [9], signal processing [10] and system identification [11, 12]. For multivariable systems, Mobayen [13] provided a robust tracking controller for multivariable delayed systems with input saturation via composite non-linear feedback; Panda and Vijayaraghavan [14] solved the parameter estimation problems of linear multivariable systems using the sequential relay feedback test; Ding [15] presented the coupled least squares identification algorithm for multivariable systems.The main feature of multivariable systems is that the input and output variables of the systems are relevant and coupled, and the difficulty of parameter estimation for such systems is that the system model both contains parameter vectors and parameter matrices, and the unmeasurable variables as well. The hierarchical identification is based on decomposition and can be applied to obtain the parameter estimates of the complex multivariable systems [16]. In this literature, Schranz et al. [17] studied the hierarchical parameter identification problems in models of respiratory mechanics; Zhang et al. [18] used the auxiliary model identification idea and the hierarchical gradient-based iterative (HGI) method for solving the parameter estimation problem of the multivariable output-error moving average (MA) systems; Han et al. [19] presented a hierarchical least squares-based iterative algorithm for multivariable systems with additive MA noises by using the decomposition technique.The iterative algorithms update the parameter estimates using the bath data and can be applied to scalar systems and multivariable systems [20], linear systems and non-linear systems [21,22]. The filtering technique can extract the useful information from noisy measurement data for parameter estimation [23,24] and has been used in signal processing and communication [25][26][27], and neural network [28,29]. The filtering techn...

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Iterative estimation for a non-linear IIR filter with moving average noise by means of the data filtering technique

Cited by 38 publications

References 25 publications

Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: Application to PMSMs

A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

Filtering‐based iterative identification for multivariable systems

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