Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Khan, Taimoor Ali; Chaudhary, Naveed Ishtiaq; Khan, Zeshan Aslam; Mehmood, Khizer; Hsu, Chung-Chian; Raja, Muhammad Asif Zahoor

doi:10.1016/j.chaos.2024.114723

Chaos, Solitons & Fractals

2024

DOI: 10.1016/j.chaos.2024.114723

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Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Taimoor Ali Khan,

Naveed Ishtiaq Chaudhary,

Zeshan Aslam Khan

et al.

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Cited by 10 publications

References 82 publications

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Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

Huang,

Luan,

Ping

et al. 2024

Intl J Robust & Nonlinear

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This paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non‐identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample‐based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non‐transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data.

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Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

Huang,

Luan,

Ping

et al. 2024

Intl J Robust & Nonlinear

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Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle

Liu,

Li,

Liu

et al. 2024

Intl J Robust & Nonlinear

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The parameter estimation problem for the nonlinear closed‐loop systems with moving average noise is considered in this article. For purpose of overcoming the difficulty that the dynamic linear module and the static nonlinear module in nonlinear closed‐loop systems lead to identification complexity issues, the unknown parameters from both linear and nonlinear modules are included in a parameter vector by use of the key term separation technique. Furthermore, an sliding window maximum likelihood least squares iterative algorithm and an sliding window maximum likelihood gradient iterative algorithm are derived to estimate the unknown parameters. The numerical simulation indicates the efficiency of the proposed algorithms.

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A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

Khan,

Chaudhary,

Hsu

et al. 2024

Chaos, Solitons & Fractals

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Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Cited by 10 publications

References 82 publications

Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

Fast Iterative Sample Transfer Identification Method for Dynamic Systems Under Non‐identical Distribution

Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle

A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

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