IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p1001-d775746.html
   My bibliography  Save this article

Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle

Author

Listed:
  • Faisal Altaf

    (Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ching-Lung Chang

    (Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Naveed Ishtiaq Chaudhary

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Khalid Mehmood Cheema

    (Department of Electrical Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan)

  • Chi-Min Shu

    (Department of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ahmad H. Milyani

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials.

Suggested Citation

  • Faisal Altaf & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Ahmad H. Milyani, 2022. "Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle," Mathematics, MDPI, vol. 10(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:1001-:d:775746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/1001/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/6/1001/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Zeshan Aslam Khan & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Hierarchical Quasi-Fractional Gradient Descent Method for Parameter Estimation of Nonlinear ARX Systems Using Key Term Separation Principle," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
    2. YoungSu Yun & Anudari Chuluunsukh & Mitsuo Gen, 2020. "Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach," Mathematics, MDPI, vol. 8(1), pages 1-19, January.
    3. Cao, Wenping & Zhu, Quanxin, 2022. "Stability of stochastic nonlinear delay systems with delayed impulses," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    4. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    5. Hasnat Bin Tariq & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification," Mathematics, MDPI, vol. 9(24), pages 1-23, December.
    6. Martin Ćalasan & Mihailo Micev & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2020. "Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Quanxin Zhu, 2022. "Nonlinear Systems: Dynamics, Control, Optimization and Applications to the Science and Engineering," Mathematics, MDPI, vol. 10(24), pages 1-2, December.
    3. Naveed Ahmed Malik & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja, 2023. "Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    4. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    5. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2022. "Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    6. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Khalid Mehmood Cheema & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdulellah Alsulami, 2023. "Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    2. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2022. "Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    3. Mingli Xia & Linna Liu & Jianyin Fang & Yicheng Zhang, 2023. "Stability Analysis for a Class of Stochastic Differential Equations with Impulses," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
    4. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).
    5. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    6. Dhayal, Rajesh & Zhu, Quanxin, 2023. "Stability and controllability results of ψ-Hilfer fractional integro-differential systems under the influence of impulses," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. R.S. Rogulin, 2021. "Model for Assessing the Effectiveness of the Formation of Sustainable Supply Chains of Raw Materials by Timber Industry Enterprises," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(1), pages 148-168.
    8. Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa Kausar & Raja, Muhammad Asif Zahoor & Chaudhary, Iqra Ishtiaq & Pinto, Carla M.A., 2022. "Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systems," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    9. Yang, Haijun & Xue, Feng, 2021. "Analysis of stock market volatility: Adjusted VPIN with high-frequency data," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 210-222.
    10. Zahra Homayouni & Mir Saman Pishvaee & Hamed Jahani & Dmitry Ivanov, 2023. "A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 395-435, May.
    11. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    12. Liu, Yiqun & Zhuang, Guangming & Zhao, Junsheng & Lu, Junwei & Wang, Zekun, 2023. "H∞.. admissibilization for time-varying delayed nonlinear singular impulsive jump systems based on memory state-feedback control," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    13. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    14. Junyu Zhang & Xinfeng Ruan & Jin E. Zhang, 2023. "Risk‐neutral moments and return predictability: International evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1086-1111, August.
    15. Tissaoui, Kais & Zaghdoudi, Taha, 2021. "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 481-492.
    16. Natalia Bakhtadze, 2023. "Preface to the Special Issue on “Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control”—Special Issue Book," Mathematics, MDPI, vol. 11(8), pages 1-3, April.
    17. Malik, Muhammad Faizan & Chang, Ching-Lung & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa kausar & Shu, Chi-Min & Raja, Muhammad Asif Zahoor, 2023. "Swarming intelligence heuristics for fractional nonlinear autoregressive exogenous noise systems," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    18. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Ousama Ben-Salha & Lamia Ben Amor, 2022. "Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models," Energies, MDPI, vol. 15(15), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:1001-:d:775746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.