Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification

Li, Feng; Zheng, Tian; He, Naibao; Cao, Qingfeng

doi:10.1109/jas.2022.105821

Cited by 25 publications

(6 citation statements)

References 23 publications

(27 reference statements)

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“…fuzzy systems [19,20], extreme learning machine [21,22], neural fuzzy model [23][24][25], and long short-term memory network [26] have been reported in the literature to construct the static nonlinear block during past years. In [17], the new form of the Kolmogorov type neural network was used for identification of Hammerstein system, the algorithm of training the network is simple, well convergent and with a small error of approximation.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

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In this paper, we present a methodology for identifying the multi-input multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented is comprised of neural fuzzy models (NFM) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, i.e., the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function based least squares method in the presence of measurable input-output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is to introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input-output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a practical nonlinear process, and it is known from the research results, that compare with existing identification techniques, the methodology utilized achieved higher identification accuracy.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

Self Cite

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“…Consider a Hammerstein‐Wiener nonlinear system, which consists of one linear block G (z −1 ), one input nonlinear block f (·), and one output nonlinear block g (·), whose structure is illustrated diagrammatically in Figure 1 43 . In this work, the two static nonlinear blocks, that us, f (·) and g (·), are approximated by two independent neural fuzzy models (NFM), 44 as described in Figure 2, and the linear dynamic block is established by finite impulse response (FIR) model.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis

Liang

et al. 2023

Intl J Robust & Nonlinear

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This article develops a novel separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein‐Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein‐Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering‐based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms.

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“…The parameter estimation of the ARX model structure presented in Figure 1 is of great interest for the research community because of its ability to model a variety of reallife problems. Saleem et al used the ARX structure to model real-life induction motor drive [50]; Azarnejad et al investigated ARX processes to study the dynamics of an actual stock returns system [51]; Hadid et al explored the practical applications of ARX in disaster management through effective flood forecasting by rainfall-runoff modelling of rivers [40]; Li et al exploited the ARX model for the modeling of practical industrial processes such as the pH neutralization process, which is normally required in wastewater treatment [52] and many other processes.…”

Section: Mathematical Model Of Arx Systemsmentioning

confidence: 99%

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

et al. 2022

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Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.

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Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification

Cited by 25 publications

References 23 publications

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

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