Fractional Wiener system identification using heuristic optimization technique based on key_term principle

Sersour, Lamia; Djamah, Tounsia; Bettayeb, Maâmar

doi:10.1016/j.ifacol.2019.11.174

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…Update the parameter estimation vector θ(t) by (13); Compute v(t) by (22) and ŵ(t) by ( 21); Obtain the parameter estimation vector θ(t), break;…”

Section: Algorithmmentioning

confidence: 99%

“…The fractional-order systems like the classical integer-order system are also suitable for block-oriented systems [22][23][24][25]. They offer a better representation of the long memory behavior and infinite dimensional structure, and the nonlinear systems can be improved by introducing the fractional order into the block structure, which also increases the difficulty of identification for block-oriented nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models

Zhang¹,

Xiang-xiang

2023

Mathematics

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Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forgetting factor stochastic gradient algorithm is deduced to improve the convergence rate. To achieve more efficient and accurate algorithms, we propose a multi-innovation forgetting factor stochastic gradient algorithm by means of the multi-innovation theory, which expands the scalar innovation into the innovation vector. To test the developed algorithms, a fractional-order dynamic photovoltaic model is employed in the simulation, and the dynamic elements of this photovoltaic model are estimated using the modified algorithms. Concurrently, a numerical example is given, and the simulation results verify the feasibility and effectiveness of the proposed procedures.

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“…Update the parameter estimation vector θ(t) by (13); Compute v(t) by (22) and ŵ(t) by ( 21); Obtain the parameter estimation vector θ(t), break;…”

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models

Zhang¹,

Xiang-xiang

2023

Mathematics

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“…30 In the application of heating processes, Hammar et al 31 researched the parameterization of the Hammerstein system by transforming the fractional-order polynomial nonlinear state-space model. Sersour et al 32 used the heuristic particle swarm optimization to identify unmeasurable internal variables combined with the key item separation technique.…”

Section: Introductionmentioning

confidence: 99%

Two‐stage gradient‐based iterative algorithms for the fractional‐order nonlinear systems by using the hierarchical identification principle

Wang

Xiao

et al. 2022

Adaptive Control & Signal

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This article focuses on the parameter estimation issues for a fractional-order nonlinear system with autoregressive noise. In the process, the challenge and difficulty are to identify the parameters of the system as well as the order. To reduce the complexity of the structure, we split the system into two subsystems by utilizing the hierarchical identification principle and derive a two-stage gradient-based iterative (2S-GI) algorithm by minimizing two criterion functions. Compared with the calculation amount of the gradient-based iterative algorithm, the computation of the 2S-GI algorithm is significantly reduced. Moreover, in order to improve the identification accuracy, we propose a two-stage moving-data-window gradient-based iterative algorithm. Finally, the simulation examples test the effectiveness of the proposed algorithms.

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“…The block-oriented nonlinear systems are popular for their simplicity and ability to accurately describe a wide variety of nonlinear systems [15,16,17,18]. The block structures, like Hammerstein systems, have the ability of flexible combination of various static nonlinear elements and various dynamic linear elements.…”

Section: Introductionmentioning

confidence: 99%

“…In the application of heating processes, Hammar et al researched the parameterization of the Hammerstein system by transforming the fractional-order polynomial nonlinear state-space model [27]. Sersour et al used the heuristic particle swarm optimization to identify unmeasurable internal variables combined with the key item separation technique [28].…”

Section: Introductionmentioning

confidence: 99%

Iterative Parameter Identification for Fractional-Order Block-Oriented Nonlinear Systems with Application to a Battery Model

Wang

Meng

2021

Preprint

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This paper investigates parameter and order identification of a class of block-oriented nonlinear systems. By using the hierarchical identification principle, the system is divided into two subsystems, which are a linear block system and a nonlinear block system. For the purpose of solving the difficulty of estimating two sets of parameter vectors, the over-parameterization method and the key item separation technique are used, respectively. Therefore, a two-stage over-parameterization gradient-based iterative algorithm and a key term separation two-stage gradient-based iterative algorithm are derived. The simulation results indicate that the proposed algorithms are effective. Finally, the proposed method is evaluated through a battery model. The results show well agreement with the real system outputs.

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Fractional Wiener system identification using heuristic optimization technique based on key_term principle

Cited by 4 publications

References 12 publications

Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models

Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models

Two‐stage gradient‐based iterative algorithms for the fractional‐order nonlinear systems by using the hierarchical identification principle

Iterative Parameter Identification for Fractional-Order Block-Oriented Nonlinear Systems with Application to a Battery Model

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