Weighted differential evolution-based heuristic computing for identification of Hammerstein systems in electrically stimulated muscle modeling

Mehmood, Ammara; Raja, Muhammad Asif Zahoor; Shi, Peng; Chaudhary, Naveed Ishtiaq

doi:10.1007/s00500-021-06701-5

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…In IN-ARX, normally, the nonlinear block is described by a polynomial-type nonlinearity, and the linear subsystem is characterized by a simple ARX structure. The IN-ARX is used to model different engineering problems arising in communication, signal processing, control systems, and biomedical sciences [8][9][10][11][12][13]. Figure 1 shows the block diagram representation of the IN-ARX model, while its mathematical representation is given in Equation ( 1)…”

Section: Input Nonlinear Autoregressive Exogenous Model Definitionmentioning

confidence: 99%

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Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

et al. 2023

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Swarm-based metaheuristics have shown significant progress in solving different complex optimization problems, including the parameter identification of linear, as well as nonlinear, systems. Nonlinear systems are inherently stiff and difficult to optimize and, thus, require special attention to effectively estimate their parameters. This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model through swarm intelligence knacks of the nonlinear marine predators’ algorithm (NMPA). A detailed comparative analysis of the NMPA with other recently introduced metaheuristics, such as Aquila optimizer, prairie dog optimization, reptile search algorithm, sine cosine algorithm, and whale optimization algorithm, established the superiority of the proposed scheme in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions represent box and whisker plots, along with the Wilcoxon rank-sum test, further confirming the reliability and stability of the NMPA for parameter estimation of IN-ARX systems.

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Section: Input Nonlinear Autoregressive Exogenous Model Definitionmentioning

confidence: 99%

“…[7]. An input nonlinear autoregressive exogenous (IN-ARX) model has been applied in different disciplines, such as multi-input multi-output (MIMO) systems [8], muscle modeling [9], data filtering [10], wireless sensor network [11], control systems [12], and electromyography [13].…”

Section: Introductionmentioning

confidence: 99%

Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

et al. 2023

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“…For example, the authors in [5] used WDE to improve the design of a multi-layer piezoelectric transducer, where WDE is adopted to get the optimal thicknesses for a non-uniform multi-layer piezoelectric transducer. The WDE is used for parameter estimation of electrically stimulated muscle systems in [6]. Although the performance and application of WDE algorithms have been further promoted, the algorithms still have difficulty in obtaining the global optimum and the effect of the initial population on the performance is rarely considered.…”

Section: Introductionmentioning

confidence: 99%

An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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The weighted differential evolution algorithm has the disadvantage of slipping into local optima and low convergence. To address these problems, an extended weighted differential evolution algorithm based on the chaotic mapping and optimal-worst dynamic opposite learning strategy is proposed. Firstly, the chaotic sequence generated by improved one-dimensional Logistic-Chebyshev mapping is employed to modify the starting population generated uniformly within the original algorithm, which aids in broadening the richness of the population. Secondly, to augment the probability of obtaining global optimum, an optimal-worst dynamic reverse learning strategy is used. Finally, a new evolutionary step size generation method is incorporated into the algorithm, which helps to acquire better solutions. The experimental outcomes obtained by testing 10 benchmark functions in CEC 2020 indicate that the proposed method has been greatly improved in accuracy.

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Parameter estimation of nonlinear systems: dwarf mongoose optimization algorithm with key term separation principle

Mehmood,

Chaudhary,

Khan

et al. 2023

J Ambient Intell Human Comput

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Weighted differential evolution-based heuristic computing for identification of Hammerstein systems in electrically stimulated muscle modeling

Cited by 12 publications

References 39 publications

Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy

Parameter estimation of nonlinear systems: dwarf mongoose optimization algorithm with key term separation principle

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