Identification of a multivariable nonlinear and time-varying mist reactor system

Cham, Chin Leei; Tan, Ai Hui; Tan, Wooi Haw

doi:10.1016/j.conengprac.2017.03.010

Cited by 19 publications

(4 citation statements)

References 19 publications

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“…13 With the development of control theory and the demand of engineering practices, the multivariable systems widely exist in all kinds of control processes. 14 Compared with single variable systems, multivariable systems have more complex structures and uncertain disturbances. 15 As a consequence, much efforts has been devoted to the identification problems of multivariable systems.…”

Section: Introductionmentioning

confidence: 99%

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Sun

Ding

et al. 2023

Adaptive Control & Signal

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This paper focuses on the parameter estimation issues of multivariate equation-error autoregressive moving average systems. By applying the gradient search and the multi-innovation theory, we derive a multi-innovation gradient based iterative (MI-GI) algorithm. In order to improve the computational efficiency and the parameter estimation accuracy, a filtering and decomposition based gradient iterative (F-D-GI) algorithm is presented by using the data filtering technique and the decomposition technique. The key is to choose an appropriate filter to filter the input-output data and to transform an original system into several subsystems. Compared with the MI-GI algorithm, the F-D-GI algorithm can generate more accurate parameter estimates. Finally, an illustrative example is provided to indicate the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Sun

Ding

et al. 2023

Adaptive Control & Signal

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“…Based on this method, Gan et al eliminated the linear parameters through the orthogonal projection and presented a variable projection algorithm for the radial basis function network-based autoregressive with exogenous inputs model [19]. During the past decade, a great deal of attention has been given to multivariable system identification [20,21] for the reason that many modern industrial processes are multivariable systems [22]. Applying the scalar system identification methods to multivariable systems may give poor performances, because multivariable systems have high-dimensional variables, complicated structures and many uncertain disturbances [23].…”

Section: Introductionmentioning

confidence: 99%

“…During the past decade, a great deal of attention has been given to multivariable system identification [19, 20] for the reason that many modern industrial processes are multivariable systems [21]. Applying the scalar system identification methods to multivariable systems may give poor performances, because multivariable systems have high‐dimensional variables, complicated structures and many uncertain disturbances [22].…”

Section: Introductionmentioning

confidence: 99%

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Liu

Ding

et al. 2019

IET Control Theory & Applications

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System identification provides many convenient and useful methods for engineering modeling. This paper targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the colored noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then we decompose the filtered system into several subsystems and develop a filtering based partially-coupled generalized extended stochastic gradient algorithm via the coupling concept. In contrast to the multivariable generalized extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

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“…In our previous works, Jia et al extended Bussgang's theorem to Hammerstein model [29][30][31], and the correlation analysis algorithm was applied to the SISO Wiener model in [32]. However, most of the real industry processes [33][34][35] are inherently multivariable systems which can describe the relationships among different variables more accurately. us, multivariable nonlinear identification techniques are required.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis Algorithm‐Based Multiple‐Input Single‐Output Wiener Model with Output Noise

Jia

2019

Complexity

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A novel identification algorithm for the correlation analysis-based multiple-input single-output (MISO) neurofuzzy Wiener model with noise is proposed. Firstly, several sets of Gaussian signals are utilized to realize the decoupling between the dynamic linear blocks and the static nonlinear blocks of a MISO Wiener system. Then, the correlation analysis is adopted to identify the parameters of the linear parts, and the problem that the output of static nonlinear block is immeasurable can be solved. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative approach used for the identification of the Wiener model. Next, the least-squares algorithm is employed to estimate the consequent parameters of the nonlinear blocks which can avoid the influence of noise. Finally, examples are used to illustrate the performance of the proposed method.

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Identification of a multivariable nonlinear and time-varying mist reactor system

Cited by 19 publications

References 19 publications

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Correlation Analysis Algorithm‐Based Multiple‐Input Single‐Output Wiener Model with Output Noise

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