Fuzzy-Evolution Computing Paradigm for Fractional Hammerstein Control Autoregressive Systems

Malik, Muhammad Faizan; Chang, Ching-Lung; Aslam, Muhammad Shamrooz; Chaudhary, Naveed Ishtiaq; Raja, Muhammad Asif Zahoor

doi:10.1007/s40815-022-01291-2

Cited by 9 publications

(1 citation statement)

References 78 publications

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“…In [32], a fractional hierarchical gradient descent method was applied for the identification of the nonlinear ARX model on several fractional order and noise conditions. In [33], genetic algorithms were applied for the identification of fractional Hammerstein ARX models in both noiseless and noisy conditions. In [34], fractional order particle swarm optimization, along with key term separation, were proposed.…”

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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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: 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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Identification of fractional Hammerstein systems with the conformable fractional derivative

Zhang,

Mi,

Zheng

2024

Adaptive Control & Signal

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SummaryIn this article, we study parameter estimation problems for fractional commensurate Hammerstein systems utilizing the conformable fractional derivative. Two algorithms are investigated: first, the Poisson moment functions (PMF) method, aiming to transfer the fractional derivative of the measurement signal into PMF using the fractional Laplace transform and convolution; second, a proposed new instrumental variable algorithm, which is based on the conformable fractional derivative. Both algorithms have been analyzed and shown to be consistent. A comprehensive complexity analysis is provided for each algorithm. Furthermore, a kind of special time‐varying systems are discussed under the conformable fractional derivative. Finally, an example is given to illustrate the effectiveness of the proposed algorithms.

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Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

Mehmood,

Chaudhary,

Khan

et al. 2023

Chaos, Solitons & Fractals

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Fuzzy-Evolution Computing Paradigm for Fractional Hammerstein Control Autoregressive Systems

Cited by 9 publications

References 78 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

Identification of fractional Hammerstein systems with the conformable fractional derivative

Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

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