IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v181y2024ics0960077924002170.html
   My bibliography  Save this article

Adaptive fuzzy echo state network optimal synchronization control of hybrid–order chaotic systems via reinforcement learning

Author

Listed:
  • Zhong, Mei
  • Huang, Chengdai
  • Cao, Jinde
  • Liu, Heng

Abstract

In this paper, a novel optimal synchronization control scheme for fractional–integer hybrid–order chaotic systems is formulated. To deal with the fractional–order (FO) constraint, a transformation programme is developed and then the master system considered as an FO chaotic system is transformed into an integer–order one. A fuzzy echo state network (FESN) with the advantages of both fuzzy logic system and echo state network is introduced to approximate system uncertainty. Simultaneously, to alleviate the resource pressure, an optimal synchronization control is proposed in the light of the reinforcement learning mechanism, in which critic–actor update laws are constructed through the negative gradient of a positive function with regard to Bellman residual. Compared to typical fuzzy synchronization control, the devised FESN synchronization scheme with the same computational complexity possess superior approximation ability and synchronization performance. Ultimately, three simulation cases are exhibited to check the validity of the proposed approach.

Suggested Citation

  • Zhong, Mei & Huang, Chengdai & Cao, Jinde & Liu, Heng, 2024. "Adaptive fuzzy echo state network optimal synchronization control of hybrid–order chaotic systems via reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924002170
    DOI: 10.1016/j.chaos.2024.114665
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924002170
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114665?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hallaji, Majid & Dideban, Abbas & Khanesar, Mojtaba Ahmadieh & kamyad, Ali vahidyan, 2018. "Optimal synchronization of non-smooth fractional order chaotic systems with uncertainty based on extension of a numerical approach in fractional optimal control problems," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 325-340.
    2. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong, 2019. "Chaotic analysis and adaptive synchronization for a class of fractional order financial system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 33-42.
    3. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    Full references (including those not matched with items on IDEAS)

    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. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    2. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Aina Maimó-Far & Alexis Tantet & Víctor Homar & Philippe Drobinski, 2020. "Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain," Energies, MDPI, vol. 13(19), pages 1-25, October.
    5. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
    6. Javad Saadat & Mohsen Farshad & Hussein Eliasi, 2023. "Optimizing Structure and Internal Unit Weights of Echo State Network for an Efficient LMS-Based Online Training," SN Operations Research Forum, Springer, vol. 4(1), pages 1-14, March.
    7. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    8. Yan, Shaohui & Wang, Ertong & Gu, Binxian & Wang, Qiyu & Ren, Yu & Wang, Jianjian, 2022. "Analysis and finite-time synchronization of a novel double-wing chaotic system with transient chaos," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    9. Peng, Qiu & Jian, Jigui, 2021. "Estimating the ultimate bounds and synchronization of fractional-order plasma chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    10. Balootaki, Mohammad Ahmadi & Rahmani, Hossein & Moeinkhah, Hossein & Mohammadzadeh, Ardashir, 2020. "On the Synchronization and Stabilization of fractional-order chaotic systems: Recent advances and future perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    11. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    12. Jian, Jigui & Wu, Kai & Wang, Baoxian, 2020. "Global Mittag-Leffler boundedness and synchronization for fractional-order chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    13. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    14. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    15. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    16. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    17. Hu, Huanling & Wang, Lin & Zhang, Dabin & Ling, Liwen, 2023. "Rolling decomposition method in fusion with echo state network for wind speed forecasting," Renewable Energy, Elsevier, vol. 216(C).
    18. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
    19. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    20. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).

    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:eee:chsofr:v:181:y:2024:i:c:s0960077924002170. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.