Neuro-fuzzy based identification method for Hammerstein output error model with colored noise

Li, Feng; Jia, Li; Peng, Daogang; Han, Chunyan

doi:10.1016/j.neucom.2017.03.026

Cited by 30 publications

(13 citation statements)

References 32 publications

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“…e consequent parameter W of each static nonlinear block is identified according to (30) and (31) in Section 3.2…”

Section: Identification Of the Static Nonlinear Blocksmentioning

confidence: 99%

“…en, the antecedent parameters of two nonlinear blocks are identified by using the clustering algorithm according to the following design parameters [41,42]: S 01 � 0.98, ρ 1 � 1.61, λ 1 � 0.02 and S 02 � 0.988, ρ 2 � 1.05, λ 2 � 0.1, which result in two neurofuzzy models whose rules are 6 and 80, respectively. Moreover, the consequent parameters W 1 and W 2 of two nonlinear blocks are obtained according to (30) and (31). e polynomial method described in [18] is also constructed by using the identical data to identify the nonlinear blocks.…”

Section: Examplesmentioning

confidence: 99%

“…Firstly, correlation analysis theorem in Section 3.1 is employed to identify the linear blocks, so the estimated linear blocks are Next, the design parameters are set as: S 01 � 0.9991, ρ 1 � 1.991, λ 1 � 0 and S 02 � 0.995, ρ 1 � 1.85, λ 1 � 0, resulting in two neurofuzzy models whose rules are 113 and 55, respectively. en, the weights W 1 and W 2 of two neurofuzzy models are obtained by using (30) and (31).…”

Section: Examplementioning

confidence: 99%

“…Goethals et al generalized the linear subspace method and combined correlation analysis with least squares support vector machines (LS-SVM) regression to estimate the parameters of the dynamics and nonlinearity representation of the Hammerstein-Wiener model [28]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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“…e consequent parameter W of each static nonlinear block is identified according to (30) and (31) in Section 3.2…”

Section: Identification Of the Static Nonlinear Blocksmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jia

2019

Complexity

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“…In recent years, many scholars have researched the static nonlinear module of the Hammerstein model, hoping to obtain a mathematical model with higher accuracy and wider applicability. Some methods to describe the nonlinear part are proposed, such as radial basis functions [20], neural fuzzy networks [21,22], polynomials [23], etc. However, these studies rarely focus on dynamic linear modules, which are based on the integer order.…”

Section: Introductionmentioning

confidence: 99%

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm.

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Parameters estimation for the Hammerstein‐Wiener models with colored noise based on hybrid signals

Li,

Han

2023

Adaptive Control & Signal

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SummaryA three‐stage estimation approach of the Hammerstein‐Wiener model with colored noise using hybrid signals is considered in this article. The Hammerstein‐Wiener model where a linear dynamic block is embedded between two static nonlinear elements, in which two nonlinear elements represented by two independent neural fuzzy models and a linear element represented by autoregressive exogenous model. The designed hybrid signals that consist of separable signals and random signals are devoted to estimating independently. First, the characteristics of separable signals in the action of the static nonlinear element are analyzed, then the output nonlinear element parameters are estimated utilizing two groups of separable signals with a multiple. Moreover, least squares based on correlation analysis method is applied to estimate linear element using one set of separable signals, which handles the interference of colored process noise. Finally, the recursive extended least squares algorithm is derived to estimate the input nonlinear element and the autoregressive moving average noise model, which improves parameters estimation accuracy owing to estimating colored noise model parameters in recursive estimate process. The feasibility of the presented estimation technique is demonstrated by an illustrative simulation example and a practical nonlinear process.

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Neuro-fuzzy based identification method for Hammerstein output error model with colored noise

Cited by 30 publications

References 32 publications

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

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

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Parameters estimation for the Hammerstein‐Wiener models with colored noise based on hybrid signals

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