Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System

Chen, Jing; Jiang, Bin; Li, Juan

doi:10.1007/s12555-016-0606-5

Cited by 18 publications

(5 citation statements)

References 38 publications

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“…Applying Lemma 1 to (27), we can conclude that E[|| θ(k s )|| 2 ] converges m.s. to a finite constant, that is, there is a constant…”

Section: ) ▪mentioning

confidence: 94%

“…The difficulty is how to choose the instrumental variables to generate the instrumental matrices 26 . Both the least‐squares‐based identification methods and the stochastic gradient‐based identification methods can be used to estimate the nonlinear system with missing data 27 . The least‐squares methods need a large amount of computation, while the stochastic gradient methods have a slow convergence rate.…”

Section: Introductionmentioning

confidence: 99%

“…26 Both the least-squares-based identification methods and the stochastic gradient-based identification methods can be used to estimate the nonlinear system with missing data. 27 The least-squares methods need a large amount of computation, while the stochastic gradient methods have a slow convergence rate. The multi-innovation identification theory can be applied to improve the parameter estimation accuracy and the convergence rate of the stochastic gradient methods by expanding the scalar innovation to an innovation vector.…”

mentioning

confidence: 99%

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Instrumental variable‐based multi‐innovation gradient estimation for nonlinear systems with scarce measurements

Xia

Miao

et al. 2022

Optim Control Appl Methods

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Summary This article considers the identification problems of nonlinear systems with scarce measurements by using the instrumental variable technique. When the product of the instrumental matrix and the information matrix is a nonsingular matrix and the weak persistent excitation condition about the instrumental vector is true, the obtained parameter estimates can be unbiased consistent estimates. The key is how to choose the instrumental variables. Difficulty arises in that the system outputs are unavailable. By applying the negative gradient search, a recursive instrumental variable‐based gradient algorithm is derived to estimate the parameters of the nonlinear systems with missing observed data. Moreover, the multi‐innovation identification theory is introduced to further improve the parameter estimation accuracy. The simulation results illustrate that the proposed methods are effective.

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“…Applying Lemma 1 to (27), we can conclude that E[|| θ(k s )|| 2 ] converges m.s. to a finite constant, that is, there is a constant…”

Section: ) ▪mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Instrumental variable‐based multi‐innovation gradient estimation for nonlinear systems with scarce measurements

Xia

Miao

et al. 2022

Optim Control Appl Methods

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“…However, the internal state vector in Equation ( 2) cannot be updated because the intersample output {y (kq − q + i) , i = 1, 2, • • • , q − 1} between two sampled outputs with slow-rate are unavailable. To solve this problem, based on the auxiliary model identification idea, [55][56][57] the missing outputs are estimated by the predicted outputs of the CRJ network, that is,…”

Section: Kq) Y (Kq) ]mentioning

confidence: 99%

Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network

Xie

Pan

Tao

et al. 2023

Intl J Robust & Nonlinear

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There are two main difficulties for identification of dual-rate sampled nonlinear systems; one is the unknown nonlinear properties and the other is the dual-rate sampled data. Considering the above two points, this paper proposes a cycle reservoir with regular jumps (CRJ) network based recursive identification algorithm. Unlike a traditional method, the proposed one does not require a priori knowledge of the nonlinearity and can handle dual-rate data; therefore, its applicability can be guaranteed. Firstly, the CRJ network is used to describe the nonlinear characteristics of the target systems. However, the update of the CRJ network requires the missing outputs, thus the auxiliary model identification theory is adopted and the missing outputs are replaced by the estimated outputs of the network. In this process, the output weights of the CRJ network are updated at a slow rate, while the estimates of the missing outputs are updated quickly, resulting in an interactive estimation computation process. Then, to improve the identification accuracy, the hyper-parameters of the CRJ network are optimized by means of the particle swarm optimization algorithm.Finally, simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.

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“…These methods can be roughly divided into two categories: on-line algorithms and off-line algorithms [12][13][14][15]. The on-line algorithm has less computational efforts but is sensitive to the newly collected data [16,17]. Due to transmission errors, process disturbances and instrument degradation, industrial data are often missing or contaminated by outliers [18].…”

Section: Introductionmentioning

confidence: 99%

Expectation Maximization Algorithm for Time-delay Output-error Models Based on Finite Impulse Response Method

Yang

Rong

et al. 2021

Int. J. Control Autom. Syst.

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In this paper, an output error (OE) model with random time delay is identified by using the expectation maximization (EM) algorithm. Since the regression model of the OE system has a colored noise, the finite impulse response (FIR) method is used to transform the OE model into an FIR model, whose regression model contains a white noise. An EM algorithm is proposed to iteratively estimate the time-delays and model parameters. Furthermore, the parameters of the OE model can be yielded based on the parameter estimates of the FIR model through the matrix transformation method. The convergence analysis and simulation results are given to illustrate the effectiveness of the proposed algorithm.

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Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System

Cited by 18 publications

References 38 publications

Instrumental variable‐based multi‐innovation gradient estimation for nonlinear systems with scarce measurements

Instrumental variable‐based multi‐innovation gradient estimation for nonlinear systems with scarce measurements

Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network

Expectation Maximization Algorithm for Time-delay Output-error Models Based on Finite Impulse Response Method

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