Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle

Wang, Xuehai; Ding, Feng

doi:10.1016/j.sigpro.2015.05.010

Cited by 73 publications

(27 citation statements)

References 39 publications

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“…Theorems 1 and 2 can be proved in a similar to the way in [37]. Table 1 The MI-ESG parameter estimates and errors for Example 1.…”

Section: And the F-misg Algorithm In (45)-(62) And (69)-(75)mentioning

confidence: 88%

“…where the order of B(z) is greater than that of D(z), i.e., n b > n d , and the basis functions are standard polynomials [37]:…”

Section: Example 1 Consider the Following H-carma Systemmentioning

confidence: 99%

“…Similar to the above F-MISG algorithm in Case 1, we can obtain the corresponding F-MISG algorithm for Case 2, where the identification algorithm of the noise model are same as the algorithm in (45)-(53), and the parameter estimate θ f (t) of the filtered identification model in(37) can be computed bŷ…”

mentioning

confidence: 99%

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A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

Mao

Ding

2015

Digital Signal Processing

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“…Theorems 1 and 2 can be proved in a similar to the way in [37]. Table 1 The MI-ESG parameter estimates and errors for Example 1.…”

Section: And the F-misg Algorithm In (45)-(62) And (69)-(75)mentioning

confidence: 88%

“…where the order of B(z) is greater than that of D(z), i.e., n b > n d , and the basis functions are standard polynomials [37]:…”

Section: Example 1 Consider the Following H-carma Systemmentioning

confidence: 99%

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A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

Mao

Ding

2015

Digital Signal Processing

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“…For comparison, we use the state observer based hierarchical stochastic gradient (SO-HSG) algorithm and the hierarchical multi-innovation stochastic gradient (SO-HMISG) algorithm in [31] and the state observer based hierarchical least squares (SO-HLS) algorithm in [32] to identify this model. In order to acquire the unique estimate, they assume that the norm of the coefficient vector γ is unity and the first coefficient is positive, i.e., …”

Section: Examplementioning

confidence: 99%

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

Ding

Xiong

et al. 2017

Adaptive Control & Signal

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SUMMARYThis paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time-delay. Both the process noise and the measurement noise are considered in the system. Based on the observable canonical state space form and the key term separation, a pseudo-linear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman-filter based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms which are missed for the time-delay, the Kalman-filter based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time-delay, parameters and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.

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“…For the sake of reducing the computational complexity, the hierarchical identification principle is utilized to transform a complex system into several subsystems and then to estimate the parameter vector of each subsystem [23,24], respectively. In this literature, Schranz et al proposed a feasible hierarchical identification process for identifying the viscoelastic model of respiratory mechanics [25].…”

Section: Introductionmentioning

confidence: 99%

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

Ding

2017

Complexity

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This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle

Cited by 73 publications

References 39 publications

A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

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

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