Gradient‐based recursive parameter estimation for a periodically nonuniformly sampled‐data Hammerstein–Wiener system based on the key‐term separation

Liu, Qilin; Ding, Feng

doi:10.1002/acs.3296

Cited by 18 publications

(13 citation statements)

References 105 publications

(105 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presented fractional adaptive algorithms can be employed to address more complicated identification problems for the nonlinear models of CARAR, HN-CAR, HN-CARMA, HN-BJ, HN-ARMAX, OE-ARMA, Hammerstein-Weiner and multi-input multi-output systems. Furthermore, the proposed fractional strategies can be combined with multi-innovation theory, 61,62 the multistage estimation theory, 63,64 the key term separation technique 65,66 to enhance the convergence rate and the estimation accuracy for system identification of the BIP systems and the generalized BIP systems.…”

Section: Discussionmentioning

confidence: 99%

Design of auxiliary model and hierarchical normalized fractional adaptive algorithms for parameter estimation of bilinear‐in‐parameter systems

Yan-cheng

Chen

et al. 2022

Adaptive Control & Signal

View full text Add to dashboard Cite

This study investigates the parameter identification issues of bilinear-in-parameter systems through fractional adaptive algorithms. An auxiliary model based 𝜀-normalized modified fractional least mean square algorithm is proposed for accelerating the parameter estimation accuracy based on the auxiliary model identification idea and the introduced convergence index, a normalized modified hierarchical fractional least mean square algorithm is presented for improving the computational efficiency based on the hierarchical identification principle. The proposed normalized fractional adaptive strategies are effective and could provide more accurate parameter estimates comparing with conventional counterparts for bilinear-in-parameter identification model based on the mean square error metrics and the average predicted output error.The effectiveness and accuracy of the proposed algorithms are further verified and validated through numerical simulations for different noise variances, fractional orders and gain parameters.

show abstract

Section: Discussionmentioning

confidence: 99%

Design of auxiliary model and hierarchical normalized fractional adaptive algorithms for parameter estimation of bilinear‐in‐parameter systems

Yan-cheng

Chen

et al. 2022

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…In order to facilitate the subsequent parameter identification, the common assumptions are listed [40][41][42].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E , compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M , the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.

show abstract

“…Considerable efforts have been made on the parameter identification for nonlinear systems in recent years 1‐3 . As a particular kind of nonlinear system, the bilinear SSM contains the coupled term of the state vector and the control vector, and has an obvious changeable structure, which can describe the nuclear reaction process, population evolution process, cell division, and others 4‐6 .…”

Section: Introductionmentioning

confidence: 99%

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Wang

Xiong

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryIn this paper, the parameter identification of bilinear state‐space model (SSM) in the presence of random outliers and time‐varying delays is investigated. Under the basis of the observable canonical form of the bilinear model, the system output can be written as a regressive form, and a bilinear state observer is applied to estimate the unknown states. To eliminate the influence of outliers and time‐varying delays on parameter estimation, we employ the Student's distribution to deal with the measurement noise and use a first‐order Markov chain to model the delays. In the framework of expectation‐maximization (EM) algorithm, the unknown parameters, delays, noise variance, states and transition probability matrix can be estimated iteratively. A numerical simulation and a continuous stirred tank reactor (CSTR) process demonstrate that the proposed algorithm has good immunity against outliers and time‐varying delays and offers good estimation accuracy for the bilinear SSM.

show abstract

Gradient‐based recursive parameter estimation for a periodically nonuniformly sampled‐data Hammerstein–Wiener system based on the key‐term separation

Cited by 18 publications

References 105 publications

Design of auxiliary model and hierarchical normalized fractional adaptive algorithms for parameter estimation of bilinear‐in‐parameter systems

Design of auxiliary model and hierarchical normalized fractional adaptive algorithms for parameter estimation of bilinear‐in‐parameter systems

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Expectation‐maximization algorithm for bilinear state‐space models with time‐varying delays under non‐Gaussian noise

Contact Info

Product

Resources

About